Optimal Classification of COVID-19: A Transfer Learning Approach

Till now COVID-19 has affected 196 countries and resulted over 446,946 cases in which 19,811 are deaths, 112,058 got recovered and still many are left to be recovered. This is a viral pneumonia and thus no antiviral drug will work to reduce these cases. During the recovery, only immune system has played a major role. Analyzing and then diagnosing is currently a major challenge. This paper focuses on the classification which can help in analysis of COVID-19 with normal chest X-ray using deep learning technique. An optimal solution has been provided using transfer learning approach keeping in mind the limitation of the dataset. The performance has been determined by train and test loss and accuracy, sensitivity, specificity and p-score. The dataset used for the classification are the x-ray images of the chest as it can help to detect novel coronavirus in patients before RT-PCR lab testing.

[1]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[3]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[7]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

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

[9]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[11]  Michael H. Goldbaum,et al.  Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification , 2018 .

[12]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).