Enhance Deep Learning-Based Framworks dor Efficient Covid-19 and Pneumonia Detection

Abstract

Covid-19, a contagious disease caused by a new virus not before encountered in humans, has clinical features in common with the flu, displaying symptoms such as cough and fever. In this research paper, we introduce a deep learning approach to rapidly detect Covid-19 and pneumonia from chest X-ray images. Using new object detection architecture, which was trained and tested on a 5,160-image dataset, the model distinguishes between non-infected patients and Covid-19 and pneumonia-affected patients. The new Convolutional Channel Spatial Attention Feature Extraction Model (CCSAFEM) obtained an accuracy of 98.70% in Covid-19 and pneumonia case classification. This shows the capability of deep learning to precisely detect respiratory diseases, an important aspect in the battle against Covid-19 and pneumonia.