INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 11, NOVEMBER 2019 ISSN 2277-8616 Deep Learning Pre-Trained Architecture Of Alex Net And Googlenet For DICOM Image Classification P.Haripriya, R.Porkodi Abstract: Deep learning is a subset of machine learning and it is dedicated to the development of machines which would learn based on the given inputs and eventually attaining Artificial Intelligence inspired by the human brain. This learning model is used to extract the complicated features from query image and increase the classification performance. In the medical domain, medical image classification is the descriptiveness and discriminative power of features extraction are critical to attain good classification performance by using traditional algorithms. Recently, Deep Learning have resulted in significant performance of medical image classification by use of the Deep Convolutional Neural Network. In this paper, Pre-trainined DCNN architecture such as AlexNet and GoogleNet are implemented and analyzed the classification performance. Pre-trained Networks are used to easily customize the model for own data set, provide state-of-the-art performance and easy access. This Experimental results are used four different significant ratio of training and testing dataset like 50:50, 60:40, 70:30 and 80:20 respectively for AlexNet and GoogleNet. The obtained result achieved the highest classification accuracy of GoogleNet is 97.02% with error rate is 0.01 in particular ratio of 70:30 when compared with other ratios and AlexNet performance results. Keywords: Deep Learning, DCNN, AlexNet, GoogleNet, Image Classification ——————————◆—————————— 1. INTRODUCTION on Tensorflow, Keras, Python along with Matplot lib for A recent advance of medical imaging devices has led to plotting data visualization. The hello world implementation producing the massive amount of image data in every day used Jupyter Notebook to build pretrained Inception v3 [1]. The medical images are stored in Digital Image Network which has accuracy greater than 94% with 65 Communication and Medicine (DICOM) format which is a training cases. The network was built for abdominal or chest very complex object due to store image data along with Meta radiograph using binary cross-entropy. This provided a data information [2]. Thus, the medical image classification foundation for experimenting deep learning on data mining is very important for diagnose and treatment purpose which projects in medical imaging. Maruyama et al., [7] presented is used to classify the historical medical images from the the comparative study for medical image classification based huge amount of data sets. So it needs an efficient medical on the clinical image inspection. The three methods which image classification architecture to classify the medical were used to compare namely SVM, ANN and CNN. Single images along with a high accuracy rate. [3] Recent Dataset from both DICOM and JPEG images were used for development of Deep learning has found many applications evaluating the results of the 3 machine learning methods. in various fields not limited to Computer Vision, Natural JPEG was understandable having less color information Language Processing, Image Processing and Automatic than the DICOM format. CNN found to be more accurate Speech Recognition. This would predominantly use both when compared with SVM and ANN. These experiments supervised and unsupervised learning with the combination performed primarily to distinguish the influence of the quality of parametric and non-parametric models. Supervised of the medical images with respect to the machine learning parametric models would be based trial and error. For image methods and tools. Zhiyun Xue et al., [8] presented a method classification, Deep Convolutional Neural Network (DCNN) for automatically identifying the gender of a person using is widely used for medical image classification and gives the front chest X-ray images. Proposal adopts CNN based deep better results compared with traditional methods. [4] This learning and transfer learning for managing the features in architecture, mainly involve the layers of Convolution, limited data. This research was inspired from datasets which Pooling layers and Fully connected layers at the end. It is was not having gender information. The sequence of steps proficient for the building of an extraction of features which is involved in the experiments were pre-processing, CNN accomplished of resolving the problems occurred in Feature extractor, Feature Selection and Classification. The classification by conventional methods. [5] The feature dataset was formed by combining data from different sources extractor of the integrated model should be able to learn to bring variety. The features extraction were tested and extracting the differentiating features from the training set of compared against Alexnet, Vggnet, GoogLENet and images accurately. ReseNet and for classification SVM and Random Forest were used. The VGGNet Feature extractor with SVM gave II. LITERATURE SURVEY classification accuracy of 86.6%. Maria Tzelepi et al., [9] Paras Lakhani et al., [6] provided a tutorial for larger developed a model retraining method for efficient audience who are interested in implementing the deep convolutional representations for content based image learning for image analysis and processing. It provides an retrieval. It proposed three retraining approaches such as full overview of the steps involved in building a deep neural unsupervised retraining, Retraining with relevance network for medical classification. The implementation done information and Relevance feedback based retraining. CNN models were used to obtain the convolution representation 3130 IJSTR©2019 www.ijstr.org INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 11, NOVEMBER 2019 ISSN 2277-8616 and build target approach for each retraining strategy. Paris The pre-trained networks are already trained in large volume 6K, UKBench, UKBench-2 datasets were used for of benchmark datasets with more number of classes and conducting the experiments. The experiments were further repurposed to extract and learn the features. The implemented using the Caffe Deep Learning framework and extracted features are transfer them to a target network to be CONV4 layers were used for feature extraction and the trained on the target dataset. For example, ImageNet which ReLU layer was replaced by PRELU layers initialized is very good for normal images can be used for training with randomly. The results were benchmarked on Precision and initial weights. Even though there might be a difference Recall results found to be satisfactory except for RRI and FU between medical image and normal images, still it would be approach. Cong Bai et al., [10] develpoed a novel method to useful to uncover generic representations which helps in address efficiency issues of large scale image retrieval. They classification networks and training convergence faster. This proposed the DCNN framework to improve its ability for study focuses on two pre-trained DCNN architecture such as feature extraction and its efficiency for similarity AlexNet and GoogleNet for DICOM image dataset. measurement. The focus of the research is to optimize AlexNet from three perspectives including Pooling Layer, AlexNet Fully Connected Layer and Hidden Layer. It also maps the The architecture used in the paper published by Alex high-dimensional features vectors to low dimensional hash Kriszhevsky in 2012 is popularly called Alexnet [11]. It code by adding hidden layer to improve the retrieval primarily solved the problem of image classification where efficiency. Upon the experiments, the efficiency of the the input image could belong 1000 different classes and implementation was done based on Retrieval time analysis output would be vector of 1000 numbers. The ith element of with three benchmark datasets MNIST, CIFAR10 and the output vector would be the probability that input image SUN397. The extraction time of images were at 0.79s, 0.48s belongs to the ith class in Network. The input image would be and 5.50s on the respective datasets and performance was in RGB format with the size of 256 x 256 and consists of 60 evaluated using Precision and mAP. million parameters and 650000 neurons. In figure 1 shows that the high level architecture of AlexNet. III. Pre – trained Network Figure 1: High level architecture of Alexnet AlexNet model mainly contains eight layers such as five for when occurre less than zero in matrix. Alexnet also solves Convolutional Layers and three for Fully Connected Layers. the problem of over fitting by applying dropout layer after It uses ReLU (Rectified Linear Unit) for the non-linear part every fully connected layers. Dropout layer has a probability and it represented by the following equation. associated with it which is applied to every neuron of the f(x) = max(0, x) response map independently. This is implemented based on The benefit of using ReLu over sigmoid is that it would help ensembles, with the help of dropout layers different set of train faster as sigmoid tend to become small in the saturating neurons are switched off and would represent different region. This causes the updates to the weights to disappear architecture which are trained in parallel with weight given to which is referred to as vanishing gradient problem. In each subset and summation of weight being one. For n AlexNet, ReLu put after each and every convolutional layers