DRESCNN: Deep RESNET Convolutional Neural Network Based Classification of X-Ray Images for Detection of COVID-19

Main Article Content

Vipul.V.Bag, V. D. Gaikwad, Mithun B. Patil, Kedar S. Swami, Sandip P. Abhang, Sonali M. Antad, Shradha Joshi-Bag


In this research work a solution is proposed to address the challenges for detection of Coronavirus disease (COVID-19) Infection through an innovative healthcare framework. The proposed system leverages predictive analytics on patient information and facilitates consultation, eliminating the delay in diagnosis and treatment. The solution is modular, encompassing various health services, thereby consolidating major healthcare concerns under one unified platform. The images are the inputs for the models provided on this platform which are passed to the preprocessing algorithm followed by passing to pretrained models or an algorithm to analyze and predict the disease. Finally, classification of the images of X-ray for COVID-19 Infection detection using Deep Convolutional Neural Network Models like ResNet50, InceptionNet, MobileNet are used at a detailed level. After classifying these X-ray images with different CNN variants, a majority voting is applied for selecting more accurate class label. It is observed that ResNet50 is giving highest accuracy frequently. Hence Deep ResNet Convolutional Neural Network (DResCNN) is used. This proposed solution not only tackles the critical issue of delayed decision-making and treatment but also introduces cost-effective measures by minimizing the need for extensive hospital visits.

Article Details