Comparative Analysis of Deep Learning Models for Detection of COVID-19 from chest X-Ray Images

Oluwadare Adepeju Adebisi¹ , John Adedapo Ojo² , Olatunde Michael Oni³

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.5 , pp.28-35, Oct-2020

Online published on Oct 31, 2020

Copyright © Oluwadare Adepeju Adebisi, John Adedapo Ojo, Olatunde Michael Oni . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The coronavirus disease is a viral infectious disease resulting from severe acute respiratory syndrome coronavirus. The new coronavirus, which began in China, in December 2019, has rapidly become pandemic and resulted to over 500000 deaths worldwide. Prompt detection of COVID-19 is necessary to prevent the transmission of COVID-19. In this research, we developed Deep Learning (DL) models for detection of COVID-19 from chest X-ray Images and evaluated the performance of the models by using accuracy, sensitivity and specificity. 401 COVID-19 chest X-ray images were obtained from open access database developed by Dr Cohen while 397 normal and 390 pneumonia chest X-ray images were obtained from Kaggle repository. Modified Alexnet, Googlenet and SqueezeNet were used to classify the chest X-ray images. Transfer learning with Alexnet achieved an overall best performance of 100% accuracy, 100% sensitivity and 100% specificity on binary test dataset and 98.31% accuracy, 98.55% sensitivity and 99.37% specificity on three classes test dataset. The work will provide early detection of COVID-19 and thereby enhance medical decisions, treatments and management procedures.

Key-Words / Index Term :
Coronavirus; X-ray Images; Deep Learning; Convolutional Neural Network; Transfer Learning

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