DOCSLIB.ORG

Review of Deep Convolution Neural Network in Image Classification

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Review of Deep Convolution Neural Network in Image Classification View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by UMP Institutional Repository 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications Review of Deep Convolution Neural Network in Image Classification Ahmed Ali Mohammed Al-Saffar, Hai Tao, Mohammed Ahmed Talab Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pahang, Malaysia [email protected] Abstract—With the development of large data age, recognition system, feature extraction methods such as Scale- Convolutional neural networks (CNNs) with more hidden layers invariant feature transform (SIFT [1]) and histogram of have more complex network structure and more powerful feature oriented gradients (HOG [2]) were used, and then the extracted learning and feature expression abilities than traditional machine Feature input classifier for classification and recognition. These learning methods. The convolution neural network model trained features are essentially a feature of manual design. For by the deep learning algorithm has made remarkable different identification problems, the extracted features have a achievements in many large-scale identification tasks in the field direct impact on the performance of the system, so the of computer vision since its introduction. This paper first researchers need to study the problem areas to be studied in introduces the rise and development of deep learning and order to design Adaptability to better features, thereby convolution neural network, and summarizes the basic model improving system performance. This period of image structure, convolution feature extraction and pooling operation of convolution neural network. Then, the research status and recognition system is generally for a specific identification task, development trend of convolution neural network model based and the size of the data is not large, generalization ability is on deep learning in image classification are reviewed, which is poor, it is difficult in the practical application of the problem to mainly introduced from the aspects of typical network structure achieve accurate identification effect. construction, training method and performance. Finally, some problems in the current research are briefly summarized and II. CONVOLUTION NEURAL NETWORK discussed, and the new direction of future development is forecasted. Deep learning is a branch of machine learning, which is one of the major breakthroughs and research hotspots in Keywords—Deep learning; convolution neural network; image machine learning in recent years. In 2006, Geoffery Hinton, a recognition; Image Classification. professor of computer science at the University of Toronto, and his student, Ruslan Salakhutdinov, published an article in the international top academic journal Science, [3], for the first I. INTRODUCTION time in the depth of learning. This paper mainly points out two Computer vision (CV) is a study of how to use computer points: (1) Artificial neural network with multiple hidden simulation of human visual science, its main task is through the layers has a very powerful feature learning ability. The collection of images (or video) analysis and understanding, to characteristics extracted by the training model have more make judgments or decisions. In the past few decades, CV has abstract and more basic expression of the original input data, (2) made great progress and development. The Image recognition By using the unsupervised learning algorithm to achieve a is a kind of technology that uses computer to process, analyze method called "layer initialization" to achieve the input data and understand the image to identify the target and object of information hierarchical expression, which can effectively different modes. It is a major research direction in the field of reduce the depth of the neural network Training difficulty. computer vision. In the image-based intelligent data acquisition Subsequently, the depth of learning in academia and industry and Processing has a very important role and impact. The use continues to heat up, in the speech recognition, image of image recognition technology can effectively deal with the recognition and natural language processing and other fields to detection and identification of specific target objects (such as obtain a breakthrough. Since 2011, the researchers first in the face, handwritten characters or goods), image classification and voice recognition problem on the application of in-deep subjective image quality assessment and other issues. learning technology, the accuracy rate increased by 20% to At present, image recognition technology has great 30%, made more than a decade the biggest breakthrough. Only commercial market and good application prospect in Internet a year later, the deep learning model based on convolution applications such as image search, commodity neural network has achieved great performance improvement recommendation, user behavior analysis and face recognition. in large-scale image classification tasks, and set off the upsurge At the same time, high-tech such as intelligent robot, of deep learning. In the literature [4], two kinds of acoustic unmanned driving and unmanned aerial vehicle Industry and modeling methods based on deep neural network are proposed, biology, medicine and geology and many other disciplines which are more effective than the traditional modeling method, have broad application prospects. In the early image and have been made larger in Uyghur's large vocabulary 978-1-5386-3849-1/17/$31.00 ©2017 IEEE 26 continuous speech recognition of the performance of the similar to a biological neural network, and the capacity of the upgrade. At present, Google, Microsoft and Facebook and model can be adjusted by changing the depth and breadth of many other Internet technology companies competed to invest the network, and has a strong assumption for natural images a lot of resources, research and development layout of large- (statistical smoothness and local Correlation) . Therefore, scale depth of learning system. CNNs can effectively reduce the learning complexity of the network model, have fewer network connections and weight In the early 1960s, Hubel and Wiesel, through the study of parameters, and are more likely to be trained than the fully cat's visual cortical system of cat, proposed the concept of connected network with a considerable size. receptive field [5] and found the hierarchical processing mechanism of information in the visual cortical pathway, Nobel Prize in Physiology or Medicine. By the mid-1980s, B. Network Structure Fukushima et al. [6], which was based on the concept of A simple convolution neural network model structure receptive field, could be seen as the first realization of diagram shown in Fig. 1, the network model consists of two Convolution neural networks (CNNs) and the first neuron- convolution layers (C1, C2) and two sub-sampling layer (S1, based Between the local connectivity and the hierarchical S2) alternately. First, the original input image is convoluted by structure of the artificial neural network. The neural cognition three trained filters (called convolution kernel) and addable machine decomposes a visual pattern into many subpatterns, bias vectors. Three feature maps are generated in the C1 layer, and these subpattern features are processed by hierarchical and then, for each feature map The localized regions are cascaded feature planes so that the model is very good even in weighted and averaged, and three new feature maps are the case of small targets of the target object Recognition ability. obtained in the S1 layer through a nonlinear activation function. After that, the researchers began experimenting with the use of These feature maps are then convoluted with the three trained an artificial neural network (actually a shallow model with only filters of the C2 layer, and three feature maps are output one hidden layer node) called a multi-layer sensor [7] instead through the S2 layer. The final output of the S2 layer is of manually extracting features and using A simple stochastic vectorized and then input into the traditional neural network for gradient descent method to train the model, and further training. proposed a back propagation algorithm for calculating the error gradient, which was subsequently proved to be very effective C. Convolution Feature Extraction [8]. In 1990, LeCun et al. [9] studied the handwritten digital Natural images have its inherent characteristics, that is, for identification problem, first proposed the use of gradient back a part of the image, its statistical characteristics and other parts propagation algorithm training convolution neural network of the same. This means that the features learned in this section model, and in MNIST [10] handwritten digital data set to show can also be used on another part, so the same learning feature relative to the time Other methods for better performance. The can be used for all positions on the image. In other words, for success of the gradient back propagation algorithm and the large-size image recognition problems, a small piece of local convolution neural network brings new hope to the machine data is randomly selected from the image as a training sample, learning field. It opens up the wave of machine learning based some features are learned from the small sample, and then on the statistical learning model, and also brings the artificial these features are used as filters, with the original
Load more

Recommended publications
  • FOR IMMEDIATE RELEASE: Tuesday, April 13, 2021 From: Matthew Zeiler, Clarifai Contact: [email protected]
    FOR IMMEDIATE RELEASE: Tuesday, April 13, 2021 From: Matthew Zeiler, Clarifai Contact: [email protected] https://www.clarifai.com Clarifai to Deliver AI/ML Algorithms on Palantir's Data Management Platform for U.S. Army's Ground Station Modernization Fort Lee, New Jersey - Clarifai, a leadinG AI lifecycle platform provider for manaGinG unstructured imaGe, video and text data, announced today that it has partnered with Palantir, Inc. (NYSE: PLTR) to deliver its Artificial IntelliGence and Machine LearninG alGorithms on Palantir’s data manaGement platform. This effort is part of the first phase of the U.S. Army’s Ground Station modernization in support of its Tactical IntelliGence TargetinG Access Node (TITAN) proGram. Palantir was awarded a Phase one Project Agreement throuGh an Other Transaction Agreement (OTA) with Consortium ManaGement Group, Inc. (CMG) on behalf of Consortium for Command, Control and Communications in Cyberspace (C5). DurinG the first phase of the initiative, Clarifai is supportinG Palantir by providinG object detection models to facilitate the desiGn of a Ground station prototype solution. “By providinG our Computer Vision AI solution to Palantir, Clarifai is helpinG the Army leveraGe spatial, hiGh altitude, aerial and terrestrial data sources for use in intelliGence and military operations,” said Dr. Matt Zeiler, Clarifai’s CEO. “The partnership will offer a ‘turnkey’ solution that inteGrates data from various sources, includinG commercial and classified sources from space to Ground sensors.” “In order to target with Great precision, we need to be able to see with Great precision, and that’s the objective of this proGram,” said Lieutenant General Robert P.
    [Show full text]
  • CSE 152: Computer Vision Manmohan Chandraker
    CSE 152: Computer Vision Manmohan Chandraker Lecture 15: Optimization in CNNs Recap Engineered against learned features Label Convolutional filters are trained in a Dense supervised manner by back-propagating classification error Dense Dense Convolution + pool Label Convolution + pool Classifier Convolution + pool Pooling Convolution + pool Feature extraction Convolution + pool Image Image Jia-Bin Huang and Derek Hoiem, UIUC Two-layer perceptron network Slide credit: Pieter Abeel and Dan Klein Neural networks Non-linearity Activation functions Multi-layer neural network From fully connected to convolutional networks next layer image Convolutional layer Slide: Lazebnik Spatial filtering is convolution Convolutional Neural Networks [Slides credit: Efstratios Gavves] 2D spatial filters Filters over the whole image Weight sharing Insight: Images have similar features at various spatial locations! Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. Fergus, Y. LeCun Slide: Lazebnik Convolution as a feature extractor Key operations in a CNN Feature maps Rectified Linear Unit (ReLU) Spatial pooling Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Key operations in a CNN Feature maps Spatial pooling Max Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Pooling operations • Aggregate multiple values into a single value • Invariance to small transformations • Keep only most important information for next layer • Reduces the size of the next layer • Fewer parameters, faster computations • Observe larger receptive field in next layer • Hierarchically extract more abstract features Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R.
    [Show full text]
  • Scene Understanding for Mobile Robots Exploiting Deep Learning Techniques
    Scene Understanding for Mobile Robots exploiting Deep Learning Techniques José Carlos Rangel Ortiz Instituto Universitario de Investigación Informática Scene Understanding for Mobile Robots exploiting Deep Learning Techniques José Carlos Rangel Ortiz TESIS PRESENTADA PARA ASPIRAR AL GRADO DE DOCTOR POR LA UNIVERSIDAD DE ALICANTE MENCIÓN DE DOCTOR INTERNACIONAL PROGRAMA DE DOCTORADO EN INFORMÁTICA Dirigida por: Dr. Miguel Ángel Cazorla Quevedo Dr. Jesús Martínez Gómez 2017 The thesis presented in this document has been reviewed and approved for the INTERNATIONAL PhD HONOURABLE MENTION I would like to thank the advises and contributions for this thesis of the external reviewers: Professor Eduardo Mario Nebot (University of Sydney) Professor Cristian Iván Pinzón Trejos (Universidad Tecnológica de Panamá) This thesis is licensed under a CC BY-NC-SA International License (Cre- ative Commons AttributionNonCommercial-ShareAlike 4.0 International License). You are free to share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the ma- terial. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. Document by José Carlos Rangel Ortiz. A mis padres. A Tango, Tomás y Julia en su memoria.
    [Show full text]
  • 1 Convolution
    CS1114 Section 6: Convolution February 27th, 2013 1 Convolution Convolution is an important operation in signal and image processing. Convolution op- erates on two signals (in 1D) or two images (in 2D): you can think of one as the \input" signal (or image), and the other (called the kernel) as a “filter” on the input image, pro- ducing an output image (so convolution takes two images as input and produces a third as output). Convolution is an incredibly important concept in many areas of math and engineering (including computer vision, as we'll see later). Definition. Let's start with 1D convolution (a 1D \image," is also known as a signal, and can be represented by a regular 1D vector in Matlab). Let's call our input vector f and our kernel g, and say that f has length n, and g has length m. The convolution f ∗ g of f and g is defined as: m X (f ∗ g)(i) = g(j) · f(i − j + m=2) j=1 One way to think of this operation is that we're sliding the kernel over the input image. For each position of the kernel, we multiply the overlapping values of the kernel and image together, and add up the results. This sum of products will be the value of the output image at the point in the input image where the kernel is centered. Let's look at a simple example. Suppose our input 1D image is: f = 10 50 60 10 20 40 30 and our kernel is: g = 1=3 1=3 1=3 Let's call the output image h.
    [Show full text]
  • Deep Clustering with Convolutional Autoencoders
    Deep Clustering with Convolutional Autoencoders Xifeng Guo1, Xinwang Liu1, En Zhu1, and Jianping Yin2 1 College of Computer, National University of Defense Technology, Changsha, 410073, China [email protected] 2 State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073, China Abstract. Deep clustering utilizes deep neural networks to learn fea- ture representation that is suitable for clustering tasks. Though demon- strating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clus- tering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization prob- lem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate
    [Show full text]
  • Tensorizing Neural Networks
    Tensorizing Neural Networks Alexander Novikov1;4 Dmitry Podoprikhin1 Anton Osokin2 Dmitry Vetrov1;3 1Skolkovo Institute of Science and Technology, Moscow, Russia 2INRIA, SIERRA project-team, Paris, France 3National Research University Higher School of Economics, Moscow, Russia 4Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow, Russia [email protected] [email protected] [email protected] [email protected] Abstract Deep neural networks currently demonstrate state-of-the-art performance in sev- eral domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train [17] format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved. In particular, for the Very Deep VGG networks [21] we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times. 1 Introduction Deep neural networks currently demonstrate state-of-the-art performance in many domains of large- scale machine learning, such as computer vision, speech recognition, text processing, etc. These advances have become possible because of algorithmic advances, large amounts of available data, and modern hardware.
    [Show full text]
  • Fully Convolutional Mesh Autoencoder Using Efficient Spatially Varying Kernels
    Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels Yi Zhou∗ Chenglei Wu Adobe Research Facebook Reality Labs Zimo Li Chen Cao Yuting Ye University of Southern California Facebook Reality Labs Facebook Reality Labs Jason Saragih Hao Li Yaser Sheikh Facebook Reality Labs Pinscreen Facebook Reality Labs Abstract Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models. 1 Introduction arXiv:2006.04325v2 [cs.CV] 21 Oct 2020 Learning latent representations for registered meshes 2, either from performance capture or physical simulation, is a core component for many 3D tasks, ranging from compressing and reconstruction to animation and simulation.
    [Show full text]
  • Geometrical Aspects of Statistical Learning Theory
    Geometrical Aspects of Statistical Learning Theory Vom Fachbereich Informatik der Technischen Universit¨at Darmstadt genehmigte Dissertation zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) vorgelegt von Dipl.-Phys. Matthias Hein aus Esslingen am Neckar Prufungskommission:¨ Vorsitzender: Prof. Dr. B. Schiele Erstreferent: Prof. Dr. T. Hofmann Korreferent : Prof. Dr. B. Sch¨olkopf Tag der Einreichung: 30.9.2005 Tag der Disputation: 9.11.2005 Darmstadt, 2005 Hochschulkennziffer: D17 Abstract Geometry plays an important role in modern statistical learning theory, and many different aspects of geometry can be found in this fast developing field. This thesis addresses some of these aspects. A large part of this work will be concerned with so called manifold methods, which have recently attracted a lot of interest. The key point is that for a lot of real-world data sets it is natural to assume that the data lies on a low-dimensional submanifold of a potentially high-dimensional Euclidean space. We develop a rigorous and quite general framework for the estimation and ap- proximation of some geometric structures and other quantities of this submanifold, using certain corresponding structures on neighborhood graphs built from random samples of that submanifold. Another part of this thesis deals with the generalizati- on of the maximal margin principle to arbitrary metric spaces. This generalization follows quite naturally by changing the viewpoint on the well-known support vector machines (SVM). It can be shown that the SVM can be seen as an algorithm which applies the maximum margin principle to a subclass of metric spaces. The motivati- on to consider the generalization to arbitrary metric spaces arose by the observation that in practice the condition for the applicability of the SVM is rather difficult to check for a given metric.
    [Show full text]
  • Pre-Training Cnns Using Convolutional Autoencoders
    Pre-Training CNNs Using Convolutional Autoencoders Maximilian Kohlbrenner Russell Hofmann TU Berlin TU Berlin [email protected] [email protected] Sabbir Ahmmed Youssef Kashef TU Berlin TU Berlin [email protected] [email protected] Abstract Despite convolutional neural networks being the state of the art in almost all computer vision tasks, their training remains a difficult task. Unsupervised rep- resentation learning using a convolutional autoencoder can be used to initialize network weights and has been shown to improve test accuracy after training. We reproduce previous results using this approach and successfully apply it to the difficult Extended Cohn-Kanade dataset for which labels are extremely sparse but additional unlabeled data is available for unsupervised use. 1 Introduction A lot of progress has been made in the field of artificial neural networks in recent years and as a result most computer vision tasks today are best solved using this approach. However, the training of deep neural networks still remains a difficult problem and results are highly dependent on the model initialization (local minima). During a classification task, a Convolutional Neural Network (CNN) first learns a new data representation using its convolution layers as feature extractors and then uses several fully-connected layers for decision-making. While the representation after the convolutional layers is usually optizimed for classification, some learned features might be more general and also useful outside of this specific task. Instead of directly optimizing for a good class prediction, one can therefore start by focussing on the intermediate goal of learning a good data representation before beginning to work on the classification problem.
    [Show full text]
  • Universal Invariant and Equivariant Graph Neural Networks
    Universal Invariant and Equivariant Graph Neural Networks Nicolas Keriven Gabriel Peyré École Normale Supérieure CNRS and École Normale Supérieure Paris, France Paris, France [email protected] [email protected] Abstract Graph Neural Networks (GNN) come in many flavors, but should always be either invariant (permutation of the nodes of the input graph does not affect the output) or equivariant (permutation of the input permutes the output). In this paper, we consider a specific class of invariant and equivariant networks, for which we prove new universality theorems. More precisely, we consider networks with a single hidden layer, obtained by summing channels formed by applying an equivariant linear operator, a pointwise non-linearity, and either an invariant or equivariant linear output layer. Recently, Maron et al. (2019b) showed that by allowing higher- order tensorization inside the network, universal invariant GNNs can be obtained. As a first contribution, we propose an alternative proof of this result, which relies on the Stone-Weierstrass theorem for algebra of real-valued functions. Our main contribution is then an extension of this result to the equivariant case, which appears in many practical applications but has been less studied from a theoretical point of view. The proof relies on a new generalized Stone-Weierstrass theorem for algebra of equivariant functions, which is of independent interest. Additionally, unlike many previous works that consider a fixed number of nodes, our results show that a GNN defined by a single set of parameters can approximate uniformly well a function defined on graphs of varying size. 1 Introduction Designing Neural Networks (NN) to exhibit some invariance or equivariance to group operations is a central problem in machine learning (Shawe-Taylor, 1993).
    [Show full text]
  • Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals Ravisutha Sakrepatna Srinivasamurthy Clemson University, [email protected]
    Clemson University TigerPrints All Theses Theses 8-2018 Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals Ravisutha Sakrepatna Srinivasamurthy Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Recommended Citation Srinivasamurthy, Ravisutha Sakrepatna, "Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals" (2018). All Theses. 2911. https://tigerprints.clemson.edu/all_theses/2911 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Science Computer Engineering by Ravisutha Sakrepatna Srinivasamurthy August 2018 Accepted by: Dr. Robert J. Schalkoff, Committee Chair Dr. Harlan B. Russell Dr. Ilya Safro Abstract In recent times, we have seen a surge in usage of Convolutional Neural Networks to solve all kinds of problems - from handwriting recognition to object recognition and from natural language processing to detecting exoplanets. Though the technology has been around for quite some time, there is still a lot of scope to do research on what's really happening 'under the hood' in a CNN model. CNNs are considered to be black boxes which learn something from complex data and provides desired results. In this thesis, an effort has been made to explain what exactly CNNs are learning by training the network with carefully selected input data.
    [Show full text]
  • Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra †
    sensors Article Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra † Sinead Barton 1 , Salaheddin Alakkari 2, Kevin O’Dwyer 1 , Tomas Ward 2 and Bryan Hennelly 1,* 1 Department of Electronic Engineering, Maynooth University, W23 F2H6 Maynooth, County Kildare, Ireland; [email protected] (S.B.); [email protected] (K.O.) 2 Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin D 09, Ireland; [email protected] (S.A.); [email protected] (T.W.) * Correspondence: [email protected] † The algorithm presented in this paper may be accessed through github at the following link: https://github.com/bryanhennelly/CNN-Denoiser-SENSORS (accessed on 5 July 2021). Abstract: Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to- noise ratio before application of statistical modeling. A popular method for performing this is Citation: Barton, S.; Alakkari, S.; Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between O’Dwyer, K.; Ward, T.; Hennelly, B. denoising and excessive smoothing of spectral peaks, the characteristics of which are critically Convolution Network with Custom important for classification purposes.
    [Show full text]