Deep-Learning Based Label-Free Classification of Activated And

sensors

Article Deep-Learning Based Label-Free Classiﬁcation of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring

Xiwei Huang * , Jixuan Liu, Jiangfan Yao, Maoyu Wei, Wentao Han, Jin Chen and Lingling Sun

Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China; [email protected] (J.L.); [email protected] (J.Y.); [email protected] (M.W.); [email protected] (W.H.); [email protected] (J.C.); [email protected] (L.S.) * Correspondence: [email protected]

Abstract: The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient’s immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.

Keywords: label-free; white blood cell classiﬁcation; deep learning; transfer learning; neutrophil activation; point-of-care

Citation: Huang, X.; Liu, J.; Yao, J.; Wei, M.; Han, W.; Chen, J.; Sun, L. Deep-Learning Based Label-Free 1. Introduction Classification of Activated and White blood cells (WBCs) are important immune cells in the human body, which are Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors mainly involved in immune response and regulation of inflammation [1]. The three-type 2021, 21, 512. https://doi.org/ differential count of WBCs, i.e., monocyte, lymphocyte, and granulocyte, provides an 10.3390/s21020512 important indication for the diagnosis of many diseases, such as leukemia [2,3] and can- cers [4]. As the dominant fraction of granulocytes, neutrophils usually show an abnormal Received: 10 November 2020 change in the expected number when dealing with damaged tissues and unresolved infec- Accepted: 11 January 2021 tions [5]. The transition from an inactivated state to an activated state is a basic condition Published: 13 January 2021 for neutrophils to function as immune cells. Therefore, the rapid classification and counting for activated and inactivated neutrophils based on the three-type WBC classification can Publisher’s Note: MDPI stays neu- provide an important reference for human immune state monitoring. tral with regard to jurisdictional clai- Traditionally, the manual method of WBC classification and counting is carried out in ms in published maps and institutio- clinics or laboratories by well-trained professionals. They usually stain the WBCs’ nuclei nal affiliations. and then perform detection, classification, and counting according to the morphological difference of the nucleus [6]. However, there are some disadvantages to the manual method. First, the required manual operation by professionals [7] and the staining process is cumbersome. Manual classification of WBCs can cause errors, including sampling errors Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. or accuracy errors due to statistical probabilities. Second, the staining process may change This article is an open access article the physiological states of WBCs, which would cause changes in their morphologies. distributed under the terms and con- WBCs’ physiological states may be transformed from inactivated to activated so that ditions of the Creative Commons At- their morphological features will be different from the original ones. On the other hand, tribution (CC BY) license (https:// automated analysis equipment, such as the Coulter Counter and Flow Cytometer, is creativecommons.org/licenses/by/ also commonly used for classification, which is based on electrical characteristics or laser 4.0/).

Sensors 2021, 21, 512. https://doi.org/10.3390/s21020512 https://www.mdpi.com/journal/sensors Sensors 2021, 21, 512 2 of 14

scattering characteristics, respectively. The disadvantages of automated analysis equipment are the high cost of the instrument and low portability [8–10]. To tackle the deficiency of manual methods and automated analysis equipment, intelligent image processing methods are applied to WBC classifications. The basic principle of image processing is to obtain the characteristic information from the WBCs images and then classify them automatically. Image processing generally can be divided into the following steps: datasets generation, pretreatment, feature extraction, and classification [11]. In particular, the recent advance of deep learning techniques has provided many powerful image processing solutions. Since the advent of Alexnet in 2012 that refreshed the record of image classification [12], deep learning based blood cell image analysis has become a hot research topic [13]. For example, Qin et al. proposed one fine-grained WBC classification framework that can classify the stained WBCs into 40 sub-categories using a self-built deep learning network. The WBC sample needs to remove the red blood cells (RBCs) in the background and then segmented each WBC into a single frame [14]. In another work, M. S. Wibawa [15] applied a simple 5-layer deep learning Convolutional Neural Network (CNN) to classify stained WBC images into four categories and compared them with machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Multi- Layer Perceptron (MLP) classifiers, to classify the feature values of WBCs, such as average brightness, entropy, kurtosis, etc. Zhao et al. extracted feature values through texture features then used the SVM classifier to divide stained WBCs into Basophil, Eosinophil, and others [16]. They finally used deep learning to extract the feature values of others and used the random forest method to classify them. However, WBC datasets in the above studies are all three-channel RGB images obtained after fluorescent staining. The staining materials affect the physiological state and morphology of WBCs, which would lead to inaccurate WBC classification. Furthermore, the WBC staining process impedes the rapid point-of-care (POC) detection application. To avoid the adverse effects of staining reagents on cell viability and cell signal transduction, label-free WBC classification is of great significance. So far, some label-free WBC classification work has been proposed in the literature and verified the feasibility of the label-free classification method. For example, Y. Li et al. proposed a lensless holographic imaging method to capture label-free WBC images and classify them based on machine learning [17]. They selected morphological features, such as cell ridge, cell diameter, etc., for holographic WBC images based on the analysis of variance (ANOVA). The over-fitting problem was reduced to a certain extent, and the feature values were classified and compared through different machine learning methods (SVM, random forest, etc.). Moreover, T.-F. Wua et al. used a microfluidic system to obtain the forward scattering of cells, and the different feature values of forwarding scattering were classified by SVM to achieve label-free classification [18]. The microfluidic system hardware facilities used in the article can obtain cell volume, membrane capacity, and optical properties of cells by measuring electrical signals, optical signals, etc., and the label-free automatic classification of cells can be performed based on these features. However, there are still challenges remaining for the existing label-free methods. First, since machine learning requires specific screening of the WBC biophysical features and manually selecting appropriate features as learning data through thresholds, the accuracy of feature selection may be limited for existing methods. Second, the existing label-free WBC classification system generally needs custom-designed hardware systems [17,18]. Using the commercially unavailable hardware to automatically classify cells is troublesome in the manufacturing process and may produce deviations due to the inaccurate manufacturing of microfluidic components. Therefore, this paper proposed a label-free WBC classification method based on deep learning to tackle the above-mentioned challenges. The novelty and contributions of our method compared with the existing label-free methods are as follows: Our label-free WBC images are directly captured by an off-the-shelf microscope when WBCs flow through a Sensors 2021, 21, x FOR PEER REVIEW 3 of 15

Sensors 2021, 21, 512 3 of 14 method compared with the existing label-free methods are as follows: Our label-free WBC images are directly captured by an off-the-shelf microscope when WBCs flow through a simple microfluidic channel and are used as training datasets for classification. No manual simple microfluidic channel and are used as training datasets for classification. No manual feature selection or complicated microfluidic device design is required. Considering that feature selection or complicated microfluidic device design is required. Considering that the feature extraction of label-free WBC images is different from fluorescently labeled the feature extraction of label-free WBC images is different from fluorescently labeled WBCs, Resent-50 with short connection structures was employed and trained to reach a WBCs, Resent-50 with short connection structures was employed and trained to reach a high accuracy of around 90%. Based on the deep transfer learning model, the classification high accuracy of around 90%. Based on the deep transfer learning model, the classification and counting of activated and inactivated neutrophils were further realized based on mor- and counting of activated and inactivated neutrophils were further realized based on phological features of aspect ratio and roundness. The proposed method enables highly morphological features of aspect ratio and roundness. The proposed method enables convenient and accurate WBC identification and minimizes the disturbance of the staining highly convenient and accurate WBC identification and minimizes the disturbance of the stainingprocess processto the WBCs. to the WBCs.Hence, Hence, it is promising it is promising for future for future label-free label-free and rapid and rapid immune immune state statemonitoring. monitoring.

2.2. MaterialsMaterials and and Methods Methods 2.1.2.1. WBCWBC Datasets Datasets Generation Generation AsAs thethe proportionproportion of RBCs in in the the whole whole bl bloodood is istoo too large large comp comparedared with with WBCs, WBCs, the theWBCs WBCs in whole in whole blood blood samples samples cannot cannot be bedirectly directly distinguished distinguished through through microscope microscope ob- observation.servation. Separating Separating WBCs WBCs from from RBCs RBCs is is an an essential essential step in generating WBCWBC datasets.datasets. ThisThis paper paper uses uses a a label-free label-free method method based based on on the the spiral spiral microfluidic microfluidic channel channel to to separate separate WBCs.WBCs. Compared Compared with with general general density density gradient gradient separation, separation, spiral spiral inertial inertial separation separation has has thethe advantages advantages of of high high throughput, throughput, fastfast processing,processing, lowlow cost,cost, and and portability, portability, and and it it can can alsoalso reduce reduce the the activation activation rate rate of of WBC WBCin in vitrovitroto to avoid avoid the the impactimpact onon thethe testtest resultsresults [[19].19]. AA more more detailed detailed spiral spiral microfluidic microfluidic channel channel discussion discussion is is beyond beyond the the scope scope of of this this article.article. WithWith spiral spiral microfluidics, microfluidics, the the WBCs WBCs were were separated separated from from peripheral peripheral blood blood samples samples that that werewere collected collected from from healthy healthy donors donors who who reported reported general general health health and and no no use use of of medical medical prescriptionsprescriptions inin thethe lastlast 2 2 weeks weeks before before enrollment enrollment and and were were purchased purchased from from Research Research BloodBlood Components,Components, LLCLLC (Boston,(Boston, MA,MA, USA).USA). TheThe separatedseparated WBCWBC werewerefluorescently fluorescently stained,stained, thenthen bright-fieldbright-field andand fluorescentfluorescent imagingimaging werewere sequentially sequentially performedperformed inin the the samesame field-of-viewfield-of-view area through through a a microscope microscope with with 100× 100 magnification× magnification objective objective lens. lens. Flu- Fluorescentorescent imaging imaging can can show show the the details details of ofWBC WBC nucleolus nucleolus features, features, and and they they are are used used to todistinguish distinguish the the actual actual type type of each of each correspon correspondingding bright-field bright-field WBC WBC image. image. Then Then the label- the label-freefree bright-field bright-field WBC WBC images images with known with known type were type used were to used form to the form training the training or testing or testingdatasets datasets for Resnet-50 for Resnet-50 model. model.The original The originalimage and image the fluorescence and the fluorescence label are labelshown are in shown in Figure1a,b. Figure 1a,b.

FigureFigure 1. 1.White White blood blood cells cells (WBC) (WBC) images images taken taken by by microscope microscope (a ()a bright-field) bright-field WBC WBC images, images, (b ()b the) correspondingthe corresponding fluorescent fluorescent WBC WBC images, images, (c–e) ( bright-fieldc–e) bright-field granulocyte, granulocyte, lymphocyte, lymphocyte, and monocyteand monocyte images, (f–h) the corresponding fluorescent granulocyte, lymphocyte, and monocyte images. images, (f–h) the corresponding fluorescent granulocyte, lymphocyte, and monocyte images.

2.2. Datasets Preprocessing The preprocessing ﬂow is shown in Figure2. This paper used the threshold segmentation method to achieve WBC and background segmentation. Threshold segmentation

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Sensors 2021, 21, x FOR PEER REVIEWmethods are widely used in image analysis applications where a signiﬁcant gap between5 of 15

the target and background pixel gray levels exist.

Datasets Separate WBC Original WBC Blood sample fluorescent generation WBCs & RBCs label images

Threshold Opening WBC images & segmentation closing segmentation Datasets preprocessing WBC images Manual Datasets filtration classification enhancement

Monocyte

Three-type WBC Input three Resnet-50 Lymphocyte classification parts of datasets

Granulocyte

Granulocyte Yes Inactivated Roundness>0.76 Convolution Activated & Inactivated filtering No No neutrophil classification Yes Proportion<1.2 Proportion Activated

Figure 2. Whole processing flowchart for WBC classification. Figure 2. Whole processing ﬂowchart for WBC classiﬁcation.

We employed the Otsu [20] method of adaptive threshold and obtained the threshold value in (1), 2 g = ω1∗ ω2 ∗ (µ1 − µ2) (1)

where g represents the final threshold value, ω1 represents the proportion of background pixels, ω2 represents the proportion of foreground pixels, µ1 represents the average grayscale value of the background, µ2 represents the average grayscale value of the foreground. After threshold segmentation, the open operation and close operation were performed on the WBC images to make the cell contour clear. Then the cell images were segmented after

determining the specific coordinates of the cell according to the cell contour. We screened the segmentedFigure 3. Examples WBC results of three-type to eliminate WBC images the cells in onthe thedatasets edges after or multiplesegmentation. adhesions to ensure that there was only one WBC in the center of an image, which is more favorable for the extraction2.3. Deep Neural of WBC Network feature Classifier values. After that, WBCs were classified manually according to their correspondingIn this paper, we fluorescent used WBC labels, image and datasets, finally, including three types 10,400 of WBCMonocytes, datasets 10,392 (granulo- Lym- cyte,phocytes, lymphocyte, and 10,266 monocyte) Granulocytes. were achieved. Among Amongthem, 80% them, of granulocytesthe WBC image include datasets basophil, were neutrophil,used as the and training eosinophil. set, while The 20% datasets were after used segmentation as the testing are set. shown The whole in Figure processing3. flow is shown in Figure 2. The Resnet-50 network was used in our task for transfer training [21], whose structure included two basic blocks, as shown in Figure 4. Resnet-50 network adds a residual unit through a short connection system. The advantages of joining the residual unit network are as follows: First, the training process connects the network characteristics of different layers. Using the globalization of the Resnet-50 network can better classify the label-free WBC images because they have more details than labeled WBC images, which typically only show the features of the nucleus. Second, the short connection mechanism can suppress phenomena, such as gradient disappearance, gradient explosion, overfitting, and network degradation. Resnet-50’s short connection makes it more “flexible” during training. The work can choose between “more convolution and nonlinear transformation” (connection) or “more inclined to do

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Datasets Separate WBC Original WBC Blood sample fluorescent generation WBCs & RBCs label images

Threshold Opening WBC images & segmentation closing segmentation Datasets preprocessing WBC images Manual Datasets filtration classification enhancement

Monocyte

Three-type WBC Input three Resnet-50 Lymphocyte classification parts of datasets

Granulocyte

Granulocyte Yes Inactivated Roundness>0.76 Convolution Activated & Inactivated filtering No No neutrophil classification Yes Proportion<1.2 Proportion Activated

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Figure 2. Whole processing flowchart for WBC classification.

Figure 3. Figure 3. ExamplesExamples ofof three-typethree-type WBCWBC imagesimages inin thethe datasetsdatasets afteraftersegmentation. segmentation.

2.3. DeepFrom Neural Table1 ,Network it can be Classifier seen that the number of various WBCs was unbalanced because the numberIn this paper, of WBCs we used in the WBC body image is unbalanced. datasets, including To improve 10,400 the Monocytes, accuracy and10,392 reduce Lym- over-fitting,phocytes, and enhanced 10,266 Granulocytes. processing of Among different them, types 80% of WBCof the datasets WBC image is necessary. datasets Thiswere paperused as used the rotation training and set, flippingwhile 20% methods were used (other as methods the testing will set. affect The the whole details processing of WBCs) flow to amplifyis shown various in Figure types 2.of The cells Resnet-50 to about network 10,000. This was method used in will our produce task for a transfer black frame. training To reduce[21], whose the impact structure of the included black frame two basic on the blocks accuracy,, as shown we used in theFigure interpolation 4. Resnet-50 method network to enlargeadds a residual the cell picture. unit throug Afterh thea short rotation, connection we uniformly system. intercepted the 200 ∗ 200 picture withoutThe a advantages black frame. of Finally, joining the the numbers residual ofunit WBCs network used are after as the follows: balancing First, process the training was obtained,process connects as shown the in network Table1. characteristics of different layers. Using the globalization of the Resnet-50 network can better classify the label-free WBC images because they have Table 1. more detailsComparison than labeled of white WBC blood images, cells (WBC) which datasets typically before only and aftershow enhancement. the features of the nucleus.WBC Second, Type the short connection Monocyte mechanism can Lymphocyte suppress phenomena, Granulocyte such as gradient disappearance, gradient explosion, overfitting, and network degradation. Resnet-50’s Segmentation 211 444 1540 shortEnhancement connection makes it more 10,400 “flexible” during training. 10,392 The work can choose 10,266 between “more convolution and nonlinear transformation” (connection) or “more inclined to do 2.3. Deep Neural Network Classifier In this paper, we used WBC image datasets, including 10,400 Monocytes, 10,392

Lymphocytes, and 10,266 Granulocytes. Among them, 80% of the WBC image datasets were used as the training set, while 20% were used as the testing set. The whole processing flow is shown in Figure2. The Resnet-50 network was used in our task for transfer training [21], whose structure included two basic blocks, as shown in Figure4. Resnet-50 network adds a residual unit through a short connection system. The advantages of joining the residual unit network are as follows: First, the training process connects the network characteristics of different layers. Using the globalization of the Resnet-50 network can better classify the label-free WBC images because they have more details than labeled WBC images, which typically only show the features of the nucleus. Second, the short connection mechanism can suppress phenomena, such as gradient disappearance, gradient explosion, overfitting, and network degradation. Resnet- 50’s short connection makes it more “flexible” during training. The work can choose between “more convolution and nonlinear transformation” (connection) or “more inclined to do nothing” (skip), or a combination of both. “Flexible” Resnet-50 network structure can better adapt to the polymorphic label-free leukocytes. Previous work using this short connection also achieved a good effect on the feature extraction of WBC [14]. Third, the residual block in the Resnet-50 network can make the network easier to train and get better weight values. It relatively eliminates the parameters of each layer in the network so that the training speed becomes faster. Sensors 2021,, 21,, 512x FOR PEER REVIEW 76 of 15 14

Figure 4. DiagramDiagram of of Resnet-50 Resnet-50 neural neural network network with with Id Identityentity Block Block shown shown on on the the bottom bottom left left and and Conv Block shown on the bottom right.

2.4. ActivatedThere are and also Inactiva someted other Neutrophil typical Classification deep learning models, such as Inception V3 or Resent-101The neutrophils. Compared can with be theclassified Inception into V3acti modelvated withoutand inactivated short connection states based structures, on the cellthe shortmorphologies. connection Inactivated structure ofneutrophils Resnet-50 are is more spherical. suitable Once for featurethere is extractionbiochemical of label-stim- ulationfree WBCs. or mechanical Resnet-101 stimulation, and Resnet-50 there only will differ be obvious in the numbermorphological of layers. changes, Considering changing the number of datasets and only three classifications we selected, a complex classification model from round to worm (some contain synapses) and become activated. Consider the char- would cause overfitting and increase the training time. For label-free WBC classification, acteristics of the morphological changes of neutrophils after activation, we used the aspect too many layers in the model will cause overfitting. Therefore, the Resnet-50 network was ratio and roundness of the cell to distinguish between activated and inactivated neutro- selected as the WBC three-type classification in our study. phils. The aspect ratio is the ratio between length and width, as shown in (4), The Resnet-50 network and transfer learning were implemented through a pre-trained model under the TensorFlow framework. Training based𝑥 on pre-trained models will reduce Aspect Ratio = training time and prevent overfitting. The weight value𝑦 in the selected pre-training model(4) was close to the local optimal value. Therefore, the pre-training weight value can be kept wherein the highx represents gradient length range and in future y represents training width for effective (the longer adjustment. one has The a diameter last layer as of long the inpre-training the default model image, was and removed, the shorter and one the has remaining a wide diameter). parameters Roun weredness reloaded, is based then on the we ratiocontinued of the trainingmeasured with cell the area output to the of circ threeumference type classifications. to determine Thethe similarity original Resnet-50 between theis suitable cell and for the three-channel circle, as shown images. in (5), But the WBC images were single-channel grayscale images, so the datasets were superimposed to convert single-channel grayscale images to three-channel grayscale images. In the model,4 ∗ 𝜋 we ∗ added 𝑎𝑟𝑒𝑎 dropout and learning rate, Roundness = (5) decreasing optimization methods. The dropout𝑝𝑒𝑟𝑖𝑚𝑒𝑡𝑒𝑟 method can increase the sparsity of the model and reduce overfitting. The learning rate decreasing method will gradually reduce wherethe learning the area rate represents during training,the area of which the image, can reduce and the the perimeter training timerepresents and find the theperime- best terlearning of the rateimage. that maximizes the gradient descent effect. If the learning rate remains the

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same, it may cause the loss value to stop decreasing without reaching the minimum. The gradient descent is a deep learning algorithm to continuously and iteratively update the model. The Root Mean Square Prop (RMSProp) algorithm was used in the model as an optimizer for gradient descent, and it solved the problem of excessive swing in the optimization loss and further accelerated the convergence speed [22]. We trained 2000 steps, set batch size to 256, dropout value to 0.5, and basic learning rate to 0.001. For every 100 steps, the learning rate dropped to 0.99 times the base learning rate. The activation function was Rectiﬁed Linear Unit (ReLU), and the objective function to be minimized was (2), ∗ min ω = arg ∑ L(yi, f (xi)) (2) ω i

The Loss value formula is shown in (3), where yi represents the label input by the model (real sample), xi represents the input of the model, f (xi) represents the predicted value of the model output, ω represents the weight value of the model, ω∗ represents the new weight value after the model has been updated.

M 1 1 L = ∑ Li = ∑ − ∑ yic log pic (3) N i N i c=1

In (3), M represents the number of categories, N represents the total sample size, yic represents the indicator variable. If the current category is the same as the sample i category, yic = 1; otherwise yic = 0. pic represents the predicted probability that sample i belongs to class sample c.

2.4. Activated and Inactivated Neutrophil Classiﬁcation The neutrophils can be classiﬁed into activated and inactivated states based on the cell morphologies. Inactivated neutrophils are spherical. Once there is biochemical stimulation or mechanical stimulation, there will be obvious morphological changes, changing from round to worm (some contain synapses) and become activated. Consider the characteristics of the morphological changes of neutrophils after activation, we used the aspect ratio and roundness of the cell to distinguish between activated and inactivated neutrophils. The aspect ratio is the ratio between length and width, as shown in (4), x Aspect Ratio = (4) y

where x represents length and y represents width (the longer one has a diameter as long in the default image, and the shorter one has a wide diameter). Roundness is based on the ratio of the measured cell area to the circumference to determine the similarity between the cell and the circle, as shown in (5),

4 ∗ π ∗ area Roundness = (5) perimeter2

where the area represents the area of the image, and the perimeter represents the perimeter of the image. For inactivated neutrophils, aspect ratio and roundness are both close to 1 [5]. But activated neutrophils will be different from inactivated neutrophils in aspect ratio and roundness value. In this paper, when calculating the aspect ratio and roundness value of the cell, convolution ﬁltering was performed on the WBC images. The convolution kernels used in convolution ﬁltering were K1 and K2:

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AspectWBCs. OriginalOriginalOriginal ConvolutionConvolutionConvolution Mask MaskMask Image Image Image Contour ContourContour AspectAspectAspect Roundness RoundnessRoundnessRoundness StatusStatus StatusStatus OriginalOriginalOriginalImage ConvolutionConvolutionConvolutionFiltering Mask MaskImageMask Image Image Image ContourImageContourContour AspectRatioAspectRatioRatioAspectRatio Roundness RoundnessRoundness StatusStatusStatus OriginalOriginalOriginal ConvolutionConvolutionConvolution Mask MaskMask Image Image Image ContourContourContour AspectRatioAspectRatioAspectRatio Roundness RoundnessRoundness StatusStatusStatus MaskMaskMask Image Image Image RatioRatioRatio RoundnessRoundnessRoundness StatusStatusStatus RatioRatioRatio 1 11 1 0.890.89 0.89 InactivatedInactivatedInactivatedInactivatedInactivated 1 1 1 0.890.890.89 InactivatedInactivatedInactivatedInactivated 1 1 1 0.890.890.89 InactivatedInactivatedInactivatedInactivated 1 1 1 0.890.890.89 InactivatedInactivatedInactivated

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3.3. R3.esults R Resultsesults 3.3. R3.esults R Resultsesults 3.3. R3.esults R Resultsesults WeWeWe se segmented segmentedgmented the the the WBC WBC WBC images, images, images, extracted extracted extracted the the the features features features of of the of the the WBCs WBCs WBCs from from from the the the training training training 3.3. R3.esults RWe ResultsResultsWeesultsWe se segmented segmentedgmented the the the WBC WBC WBC images, images, images, extracted extracted extracted the the the features features features of of theof the the WBCs WBCs WBCs from from from the the the training training training setset setthrough Wethrough throughWeWe se segmented sethegmented gmentedthe the Resnet-50 Resnet-50 Resnet-50 the the the WBC WBC network, WBC network, network,images, images, images, and andextracted and extracted classifi extracted classifi classified theed the ed themthe features them featuresthem features into into into of three of three theof three the the WBCscategories. 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Thisexamination.This training training training was was was carried carried carried out out out under under under a 64-bia a64-bi 64-bitt LinuxLinuxt tLinux Linux system,system, system, system, usingusing using using NvidiaNvidia Nvidia Nvidia KeplerKepler Kepler Kepler K80K80 K80 K80 icalicalexamination.ical examination.This examination. Thisexamination.This training training training was was was carried carried carried out out out under under under a a64-bi a64-bi 64-bit Linuxt tLinux Linux system, system, system, using using using Nvidia Nvidia Nvidia Kepler Kepler Kepler K80 K80 K80 GPUGPUGPUThis acceleratorThis acceleratorThis accelerator training training training card, wascard, wascard, was ancarried ancarried ancarriedeight-channel eight-channel eight-channel out out out under under under high-p a high-p 64-biahigh-p a64-bi 64-bierformancett erformanceLinuxLinuxterformance tLinux Linux system,system, system, system,SAS SAS SAS usingRAIDusing usingRAID usingRAID NvidiaNvidiacard Nvidiacard Nvidiacard (4 (4 KeplerKepler GB(4 Kepler GB KeplerGB cache), cache), cache),K80K80 K80 K80 GPUGPUGPUThis acceleratorThis acceleratorThis accelerator training training training card, wascard, card,was was ancarried ancarried carriedaneight-channel eight-channel eight-channel out out outout under under underunder high-p a high-p a64-bihigh-p aa64-bi 64-bit64-bierformancet erformanceLinuxterformance tLinux LinuxLinux system, system, system, system,SAS SAS SAS usingRAID usingRAID usingusingRAID Nvidiacard Nvidiacard NvidiaNvidiacard (4 (4 Kepler GB(4 Kepler GB KeplerKeplerGB cache), cache), cache),K80 K80 K80K80 GPUGPUGPU accelerator accelerator accelerator card, card, card, an an aneight-channel eight-channel eight-channel high-p high-p high-performanceerformanceerformance SAS SAS SAS RAID RAID RAID card card card (4 (4 GB(4 GB GB cache), cache), cache), GPUGPUGPU accelerator accelerator accelerator card, card, card, an an aneight-channel eight-channel eight-channeleight-channel high-p high-p high-performancehigh-performanceerformanceerformance SAS SAS SAS RAID RAID RAID card card card (4 (4 GB (4 GB GB cache), cache), cache), and 16 G ECC Registered DDR4 2133 memory. The code was written using Python 2.7, TensorFlow 1.13.1 version. We used a confusion matrix to obtain Precision, Recall, and F1_Score for evaluation. The confusion matrix was used to summarize the result of a classifier. For classification, the confusion matrix was a k ∗ k table, which was used to record the relationship between the predicted result and the actual label. The Recall formula is shown in (6), which represents the probability of being predicted as a positive sample in a sample that was positive. The Sensors 2021, 21, 512 9 of 14

formula for the Precision is shown in (7), which represents the probability of a positive sample among all the samples predicted to be positive. The higher Recall and Precision are, the better the classification results we get, but they are also a pair of contradictions. F1_Score, as shown in (8), is a trade-off between them. Both Precision and Recall estimate positive samples, and the overall estimate still uses Accuracy as the standard. The formula of Accuracy is as shown in (9). Among them, True Positive (TP) means positive samples predicted to be positive, True Negative (TN) means negative samples predicted to be negative, False Positive (FP) means negative samples predicted to be positive, and False Negative (FN) represents a positive sample predicted to be negative. We also used 95% confidence intervals (CI) to evaluate the reliability of Accuracy. The formula of CI is as shown in (10), where n represents the number of Accuracy values selected, µˆ represents the average of selected Accuracy values, σ represents the standard deviation of selected Accuracy values, and M represents the number of 95% confidence intervals. We also applied the Receiver Operating Characteristic (ROC) curve to evaluate the model, where the vertical axis of the ROC curve is True Positive Rate (TPR) as in (11), and the horizontal axis is False Positive Rate (FPR) as in (12). The ROC curve can accurately reflect the relationship between TP and FP, which is a comprehensive representation of the detection accuracy. The ROC curve expresses the correctness of the selected eigenvalues. The closer the deviation of the curve to the (0,1) point, the more successful the feature value selection is. The Area Under the ROC Curve (AUC) value refers to the area of the ROC curve. The larger the AUC value is, the more successful model feature value selection is. TP Recall = (6) TP + FN TP Precision = (7) TP + FP 2 ∗ Precision ∗ Recall F1_Score = (8) Precision + Recall TP + TN Accuary = (9) TP + TN + FP + FN σ σ P µˆ − 1.96 √ ≤ M ≤ µˆ + 1.96 √ ≈ 0.95 (10) n n TP TPR = (11) TP + FN FP FPR = (12) TP + TN After 2000 steps of training, it was finally concluded that the accuracy of the Resnet-50 training set was about 95%, and the accuracy of the Resnet-50 testing set could reach more than 90%. The 95% confidence interval of the testing set was [0.943 − 0.004, 0.943 + 0.004]. Testing accuracy was close to the training accuracy, indicating that there was no overfitting phenomenon in Resnet-50. The value of Accuracy was relatively stable in both testing and training sets, so it proved the effectiveness of the Resnet-50 model. Besides, the Recall and Precision of the Resnet-50 testing set were about 90% and 96%, respectively, F1_Score of the testing set was about 93%, which proved that the relationship between Recall and Precision was relatively balanced. The curves of Loss and Accuracy are shown in Figure5. After 1000 steps, the loss value of the Resnet-50 testing set fluctuated greatly around 0.3. This could be caused by the fact that the label-free WBC images have more feature values and less regular than the fluorescently labeled WBCs. Sensors 2021, 21, x FOR PEER REVIEW 10 of 15

overfitting phenomenon in Resnet-50. The value of Accuracy was relatively stable in both testing and training sets, so it proved the effectiveness of the Resnet-50 model. Besides, the Recall and Precision of the Resnet-50 testing set were about 90% and 96%, respectively, F1_Score of the testing set was about 93%, which proved that the relationship between Recall and Precision was relatively balanced. The curves of Loss and Accuracy are shown in Figure 5. After 1000 steps, the loss value of the Resnet-50 testing set fluctuated greatly around 0.3. This could be caused by the fact that the label-free WBC images have more feature values and less regular than the fluorescently labeled WBCs. We used two other transfer learning models for comparative analysis, including In- ceptionV3 network and Resnet-101 network with pre-trained models (the pre-trained Sensors 2021, 21, 512models are all from ImageNet). As a classic network model, InceptionV3 has been applied 10 of 14 to classification tasks in many works. The only difference between Resnet-101 and Resnet- 50 is the number of their layers.

Figure 5. ComparisonFigure 5. Comparison results of Loss results and Accuracyof Loss and during Accuracy training during and testingtraining for and Inception testing for V3, Inception Resnet-101, V3, and Resnet-50: (a) is the trainingResnet-101, loss, (b and) is theResnet-50: testing ( loss,a) is (thec) is training the training loss, accuracy,(b) is the testing and (d) loss, is the (c testing) is the accuracy.training accuracy, and (d) is the testing accuracy. We used two other transfer learning models for comparative analysis, including Incep- From the comparisontionV3 network of the and loss Resnet-101 values of networkthe training with set pre-trained and the testing models set (thein Figure pre-trained models 5a,b, it can be seenare all that from the ImageNet).loss value of Asthe a Resnet-50 classic network model was model, the InceptionV3lowest. In the has accu- been applied to racy comparisonclassification of the training tasks set in manyand testin works.g set The of onlyFigure difference 5c,d, although between the Resnet-101 accuracy and Resnet-50 of Resnet-50 andis the Resnet-101 number of was their almost layers. the same, it can be seen from the comparison value of the three modelsFrom thein Figure comparison 6 that th ofe F1_score the loss of values Resnet-50 of the was training the highest, set and which the testing set in proved that theFigure overall5a,b, performance it can be seen of thatResnet-50 the loss was value better of the than Resnet-50 the other model two wasmodels. the lowest. In the In addition, theaccuracy 95% confidence comparison interval of the of training the Resnet-50, set and Resnet-101, testing set of and Figure InceptionV35c,d, although are the accuracy shown in Tableof Resnet-503. The fluctuation and Resnet-101 of Resnet-50 was almost (±0.004 the same,) was itless can than be seen the from Resnet-101 the comparison value (±0.005) and theof theInceptionV3 three models (±0.008 in Figure), which6 that proved the F1_score that the of accuracy Resnet-50 of was Resnet-50 the highest, was which proved more stable thanthat the the other overall two performance models. It can of Resnet-50 also be seen was in better the comparison than the other of two the models.ROC In addition, curve as in Figurethe 95% 7 that confidence the curve interval of Resn ofet-50 the Resnet-50,was the closest Resnet-101, to the and(0,1) InceptionV3 point. The are shown in straight-line y Table= x in3 Figure. The fluctuation 7 represents of the Resnet-50 result of ( ±a 0.004classifier) was using less thana random the Resnet-101 guessing (±0.005) and strategy. It canthe be InceptionV3 seen that the (± Resnet0.008), whichmodel provedwas on thatthe theROC accuracy curve above of Resnet-50 the y = was x more stable straight line, whichthan theproves other that two the models. Resnet It model can also was be better seen inthan the the comparison randomly ofguessed the ROC curve as in classifier. The Figurereason7 forthat the the poor curve performa of Resnet-50nce of wasthe InceptionV3 the closest to model the (0,1) could point. be that The straight-line y = x in Figure7 represents the result of a classifier using a random guessing strategy. It can be seen that the Resnet model was on the ROC curve above the y = x straight line, which proves that the Resnet model was better than the randomly guessed classifier. The reason for the poor performance of the InceptionV3 model could be that there is no residual network structure in the model and the feature value extraction is not accurate enough. Resnet-101 is deeper than Resnet-50, but the loss value was not lower than Resnet-50, and the value of F1_Score and AUC were not better than Resnet-50, indicating that increasing more layers may not be effective. From the data of the confusion matrix in Figure8, it can be seen that the classification accuracy of granulocyte (95.6%) and lymphocyte (94%) were high, while the monocyte (84.3%) was low. It indicated that the characteristics of monocyte cells after training were not very obvious and were easily misidentified as lymphocytes. The above results prove that it is feasible to use the Resnet-50 network with a pre-training model to realize a three-type classification for WBCs. Sensors 2021, 21, x FOR PEER REVIEW 11 of 15

there is no residual network structure in the model and the feature value extraction is not accurate enough. Resnet-101 is deeper than Resnet-50, but the loss value was not lower than Resnet-50, and the value of F1_Score and AUC were not better than Resnet-50, indicating that increasing more layers may not be effective. From the data of the confusion matrix in Figure 8, it can be seen that the classification accuracy of granulocyte (95.6%) Sensors 2021, 21, x FOR PEER REVIEWand lymphocyte (94%) were high, while the monocyte (84.3%) was low. It indicated11 ofthat 15

Sensors 2021, 21, 512 the characteristics of monocyte cells after training were not very obvious and were11 easily of 14 misidentified as lymphocytes. The above results prove that it is feasible to use the Resnet- 50 network with a pre-training model to realize a three-type classification for WBCs. there is no residual network structure in the model and the feature value extraction is not accurate enough. Resnet-101 is deeper than Resnet-50, but the loss value was not lower than Resnet-50, and the value of F1_Score and AUC were not better than Resnet-50, indicating that increasing more layers may not be effective. From the data of the confusion matrix in Figure 8, it can be seen that the classification accuracy of granulocyte (95.6%) and lymphocyte (94%) were high, while the monocyte (84.3%) was low. It indicated that the characteristics of monocyte cells after training were not very obvious and were easily misidentified as lymphocytes. The above results prove that it is feasible to use the Resnet- 50 network with a pre-training model to realize a three-type classification for WBCs.

FigureFigure 6. 6.Comparison Comparison results results of of Recall, Recall, Precision, Precision, F1_score F1_score for for three three models. models.

Table 3. Comparison of Conﬁdence Intervals (CI) and Average Accuracy for different models.

Model 95% Conﬁdence Intervals Average Accuracy Resnet-18 – 0.649 InceptionV3 [0.767 − 0.008, 0.767 + 0.008] 0.767 Resnet-101 [0.9126 − 0.005, 0.9126 + 0.005] 0.9126 Resnet-50 [0.943 − 0.004, 0.943 + 0.004] 0.943 Figure 6. Comparison results of Recall, Precision, F1_score for three models.

Figure 7. Comparison results of ROC curves for the three models.

FigureFigure 7.7. ComparisonComparison resultsresults ofof ROCROC curvescurves forfor thethe threethree models.models. Based on the automatic classiﬁcation and counting of the activated and inactivated

WBCs, a total of 1771 granulocyte cells were tested, the number of activated cells obtained was 365, and the number of inactivated cells was 1328. According to the ratio of basophil and eosinophil, they account for 1% and 5% of the entire granulocytes, respectively. There- fore, the number of activated neutrophils was about 346, and the number of inactivated neutrophils was about 1261.

Sensors 2021, 21, 512 12 of 14 Sensors 2021, 21, x FOR PEER REVIEW 12 of 15

FigureFigure 8. ComparisonComparison results results of of the confusion matrix for the three models.

4. DiscussionsBased on the automatic classification and counting of the activated and inactivated WBCs,Similar a total work of 1771 [24 granulocyte] has used the cells similar were te Resnet-18sted, the number model to of classify activated label-free cells obtained WBCs was(neutrophils 365, and the and number monocytes) of inactivated with the cells highest was accuracy1328. According of 64.9%, to the but ratio the accuracyof basophil of andour eosinophil, method reached they account about 90%,for 1% as and shown 5% of in the Table entire3. granulocytes, The article pointed respectively. out that There- the fore,reason the for number the unsatisfactory of activated modelneutrophils was the wa imbalances about 346, of and the datasetthe number and lowof inactivated resolution neutrophilsof the obtained was images.about 1261. The impact of dataset imbalance on Accuracy is also reflected in the Resnet-50 model: we found that the Recall of monocyte was not very high, so it 4.was Discussions sometimes recognized as a lymphocyte, but other types of WBCs did not have such problems.Similar This work could [24] be has due used to thethe imbalancesimilar Resnet-18 of the initial model dataset to classify since label-free the monocyte WBCs in (neutrophilsthe human blood and monocytes) at the time ofwith sampling the highest is less. accuracy Although of 64.9%, we have bu expandedt the accuracy the dataset, of our methodthe real featuresreached ofabout the monocyte90%, as shown were stillin Table less than3. The the article other pointed two classes, out that so the the accuracy reason forof thethe monocyteunsatisfactory was relativelymodel was low. the imbalance of the dataset and low resolution of the obtainedThere images. are probably The impact three of reasons dataset for imbalanc our highere on accuracy Accuracy results. is also First, reflected the WBC in the image Res- net-50resolution model: used we was found higher that (obtained the Recall by of a 100mo×nocyteobjective was microscope),not very high, compared so it was with some- the timescell resolution recognized taken as a bylymphocyte, flow cytometry but other in [ 23ty],pes the of resolutionWBCs did ofnot our have dataset such wasproblems. much Thishigher. could Second, be due the to Resnet-50 the imbalance model of we the used initial had dataset more layers since thanthe monocyte the Resnet-18 in the model, human so bloodthe extracted at the time feature of sampling values were is less. more Although accurate. we Third, have the expanded pre-training the modeldataset, we the added real featureswas helpful of the to monocyte improve the were accuracy. still less Compared than the other with two the deepclasses, learning so the modelaccuracy that of uses the monocytestained WBC was images relatively as a low. dataset, the results of our model are also impressive. In previous work [25], the four-layer model built by itself was used to classify the stained WBCs, in Tablewhich 3. the Comparison obtained of Accuracy Confidence and Intervals Recall were (CI) and similar Average to our Accuracy experimental for different results. models. Previous work [26] also used deep learning methods to extract features of stained WBCs, the author used Lenet-5,Model Alexnet model 95% forConfidence comparison, Intervals and finally used theAverage self-built Accuracy WBCsNet structureResnet-18 to get the highest accuracy rate-- of 93%. During the experiment, 0.649 we also tried to useInceptionV3 Lenet-5 and Alexnet’s[0.767 migration − 0.008, learning0.767 + 0.008] models for three classifications. 0.767 Because theResnet-101 feature values of label-free[0.9126 cells− 0.005, are 0.9126 more difficult + 0.005] to extract, the effect 0.9126 was not good for modelsResnet-50 with fewer layers.[0.943 Our− 0.004, classification 0.943 + 0.004] results based on unstained 0.943 WBC were similar to those based on stained WBC, indicating the feasibility of our label-free WBC classificationThere are method. probably three reasons for our higher accuracy results. First, the WBC image resolutionHowever, used our method was higher of extracting (obtained feature by a 100× values objective using the microscope), Resnet-50 model compared can still with be thefurther cell resolution improved. taken In [27 ],by it flow proposed cytometry the use in of[23], the the IDEAS resolution tool to of extract our dataset the characteristic was much higher.values ofSecond, five types the ofResnet-50 WBCs, and model the accuracywe used reachedhad more was layers about than 97%. the Similarly, Resnet-18 in [model,17], the soproposed the extracted manual feature extraction values of feature were valuesmore accurate. method toThird, classify the WBCs pre-training had an accuracymodel we of added99%. Compared was helpful with to machineimprove learningthe accuracy. methods, Compared Resnet-50 with is notthe verydeep accurate learning in model extracting that usesfeature stained values, WBC resulting images in as the a classificationdataset, the re accuracysults of beingour model not as are high also as usingimpressive. machine In previouslearning orwork even [25], manual the four-layer classification. model Therefore, built by the itself automatic was used extraction to classify of WBC the featuresstained using deep learning requires further improvement. Although the accuracy of the Resnet-50 WBCs, in which the obtained Accuracy and Recall were similar to our experimental remodel is not prominent, it can be used as a method of initial medical diagnosis. In some sults. Previous work [26] also used deep learning methods to extract features of stained remote and resource-limited places where cell sorting equipment or manual sorting conditions WBCs, the author used Lenet-5, Alexnet model for comparison, and finally used the self- are lacking, this method provides a convenient solution. built WBCsNet structure to get the highest accuracy rate of 93%. During the experiment,

Sensors 2021, 21, 512 13 of 14

In terms of the method used in this paper to distinguish activated and inactivated neutrophils, it was based on obvious changes in morphological characteristics. The ideal result can be obtained by simple convolution filtering and calculation of cell characteristics on the cell image with less time consumption. Its advantages are high speed and low requirements for computer performance. The fast testing speed can be reflected in the testing of 1771 cell images in only 60 s. If the deep learning method is used for testing, the training process and the test process will take a long time, and the computer configuration requirements are relatively high. The lower error rate is also a major advantage of this method. If deep learning or machine learning is used for two-class training, it will increase the difficulty and time consumption. So this is a more convenient and concise method. The disadvantage is that the total number of cells tested was 1771, but the total number of activated cells and inactivated cells was only 1693, which proves that this system was insufficient to identify the contours of cells because some cells were not detected, so that the predicted number was smaller than the total number before the test. In this respect, further improvement is still needed.

5. Conclusions This paper proposed a label-free three-type WBC classification method using the transfer learning technique based on the Resnet-50 neural network. The features of label- free WBCs could be extracted and divided into three categories, such as granulocyte, lymphocytes, and monocytes. The activated and inactivated neutrophils, which are the dominant granulocytes, could be detected for immune state monitoring. The Resnet-50 model-based method was faster and more convenient than other machine learning or manual detection methods. The accuracy of this article can reach about 90%, which verifies the feasibility of using label-free WBC images for classification. Although the accuracy is not as high as that of manually extracting feature values using machine learning, our method is more convenient and time-saving. The research in this article is helpful for the development of label-free cell classification methods. Using label-free cell images for classification is becoming a future trend. In future work, we will improve the network so that it will be more suitable for the extraction of label-free WBC feature values to increase the accuracy of the results.

Author Contributions: Conceptualization, X.H.; Data curation, J.L.; Formal analysis, J.L.; Funding acquisition, X.H. and L.S.; Investigation, J.L.; Methodology, X.H. and J.L.; Resources, L.S.; Software, X.H. and J.L.; Supervision, X.H.; Validation, J.Y., M.W., W.H., and J.C.; Writing–review & editing, X.H., J.Y., and M.W. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 61827806), Qianjiang Talent Project Type-D of Zhejiang (Grant No. QJD1802021), Fundamental Research Funds for the Provincial Universities of Zhejiang (Grant No. GK209907299001-305), and 2020 Talent Cultivation Project of Zhejiang Association for Science and Technology (Grant No. CTZB-2020080127-19). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Hangzhou Dianzi University (protocol code 20200608YY001). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data available on request due to privacy restrictions. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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