Online ISSN: 2515-8260

Keywords : Binary Classification

Improved Convolution Neural Network For Detecting Covid-19 From X-Ray Images

Sankara Sai Sumanth Kota; Anthony Rajesh Reddy; YeruvaRohit Desai; Venubabu Rachapudi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 1221-1230

Coronavirus disease 2019 (COVID-19) is a communicable disease caused by coronavirus 2 (SARS-CoV-2), a severe acute respiratory syndrome. It was first identified in Wuhan, Hubei, China in December 2019 and has contributed to a continuing pandemic. As of early July 2020, more than 10.6 million cases throughout 188 countries around the world were identified culminating in much more than 516,000 deaths. To prevent COVID-19 from spreading among people, an automated detection system needs to be introduced as a fast-alternative diagnosis method. Machine learning algorithms based on radiographic images can be used as mechanism to support decision taking and help radiologists speed up the diagnostic process. This work introduces a new paradigm for automatic detection of COVID-19 using raw X-ray images in the chest. The proposed model with 4 Convolutional Layers, 2 Max Pooling Layers and Drop Outs, is designed to provide reliable diagnostics for binary (COVID vs. No-Findings) and multi-class (COVID vs. No-Findings vs. Pneumonia) diagnosis. Our model provided gives 98.9% Binary Classification accuracy and 85% Multi Classification accuracy.