Deep learning model for detection of COVID-19 utilizing the chest X-ray images

Cogent Engineering

Niranjana Sampathila¹  Shahanaz Abdul Gafoor¹  Swathi K S²  Madhushankara M³ 

[1] Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India;Manipal Institute of Management, Manipal Academy of Higher Education (MAHE), Manipal, India;Manipal School of Information Sciences, Manipal Academy of Higher Education (MAHE), Manipal, India;

关键词: deep learning;    convolutional neural networks (CNN);    chest X-ray;    COVID-19;    pandemic;   

DOI  :  10.1080/23311916.2022.2079221

来源: DOAJ

【 摘 要 】

The COVID-19 pandemic has caused more than 200 million infected cases and 4 million deaths across the world. The pandemic has triggered a massive epidemic, with a significant effect on the health and lives of many people worldwide. Early detection of this disease is very important for maintaining social well-being. Generally, the RT-PCR test is a diagnosis method used for the detection of the COVID-19, yet it is not the only reliable diagnostic tool. In this study, we discuss the image-based modalities for the detection of coronavirus utilizing Deep Learning methodology, which is one of the most innovative technologies today and has proven to be an efficient solution for a number of medical conditions. Coronavirus affects the respiratory tract of individuals. One of the best ways is to identify this disease from chest radiography images. Early research demonstrated unique anomalies in chest radiographs of covid-positive patients. By using Deep Learning Multi-layered networks, we classified the chest images as covid positive or negative. The proposed model uses the dataset of patients infected with Coronavirus, in which the radiologist indicated multilobar involvements in the chest X-rays. A total of 6500 images have been considered for the study. The convolutional network (CNN) model was trained and a validation accuracy of 94% is obtained.

【 授权许可】

Unknown