Improved convolution neural network for detecting Covid-19 from X-ray images
Year: 2020
Type: article
Abstract: 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
Authors: Srinivas Kota, Anuradha Reddy, Yogita K. Desai, Venubabu Rachapudi
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W3111778169