A comparative study of deep learning models for medical image classification

Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities. Neurosciences has already exploited DL techniques, thus portrayed itself as an inspirational source for researchers exploring the domain of Machine learning. DL enthusiasts cover the areas of vision, speech recognition, motion planning and NLP as well, moving back and forth among fields. This concerns with building models that can successfully solve variety of tasks requiring intelligence and distributed representation. The accessibility to faster CPUs, introduction of GPUs-performing complex vector and matrix computations, supported agile connectivity to network. Enhanced software infrastructures for distributed computing worked in strengthening the thought that made researchers suffice DL methodologies. The paper emphases on the following DL procedures to traditional approaches which are performed manually for classifying medical images. The medical images are used for the study Diabetic Retinopathy(DR) and computed tomography (CT) emphysema data. Both DR and CT data diagnosis is difficult task for normal image classification methods. The initial work was carried out with basic image processing along with K-means clustering for identification of image severity levels. After determining image severity levels ANN has been applied on the data to get the basic classification result, then it is compared with the result of DNNs (Deep Neural Networks), which performed efficiently because of its multiple hidden layer features basically which increases accuracy factors, but the problem of vanishing gradient in DNNs made to consider Convolution Neural Networks (CNNs) as well for better results. The CNNs are found to be providing better outcomes when compared to other learning models aimed at classification of images. CNNs are favoured as they provide better visual processing models successfully classifying the noisy data as well. The work centres on the detection on Diabetic Retinopathy-loss in vision and recognition of computed tomography (CT) emphysema data measuring the severity levels for both cases. The paper discovers how various Machine Learning algorithms can be implemented ensuing a supervised approach, so as to get accurate results with less complexity possible.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[3]  Lauge Sørensen,et al.  Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns , 2010, IEEE Transactions on Medical Imaging.

[4]  Yoshua Bengio,et al.  Joint Training of Deep Boltzmann Machines , 2012, ArXiv.

[5]  Abdul Kawsar Tushar,et al.  Handwritten Arabic numeral recognition using deep learning neural networks , 2017, 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR).

[6]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[7]  CireşAnDan,et al.  2012 Special Issue , 2012 .

[8]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[9]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[10]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[11]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[12]  Asger Dirksen,et al.  Identification of patients with chronic obstructive pulmonary disease (COPD) by measurement of plasma biomarkers , 2008, The clinical respiratory journal.

[13]  Sejin Lee.  Deep learning of submerged body images from 2D sonar sensor based on convolutional neural network , 2017, 2017 IEEE Underwater Technology (UT).

[14]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[15]  Kaisheng Yao,et al.  KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Patrice Y. Simard,et al.  High Performance Convolutional Neural Networks for Document Processing , 2006 .

[17]  Meghna Babbar-Sebens,et al.  Fuzzy and deep learning approaches for user modeling in wetland design , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[18]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[19]  Ebru Arisoy,et al.  Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[20]  M. Dinakaran,et al.  A generic framework for ontology-based information retrieval and image retrieval in web data , 2016, Human-centric Computing and Information Sciences.

[21]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..