%R 10.4018/978-1-6684-6523-3.ch005
%J Structural and Functional Aspects of Biocomputing Systems for Data Processing
%T Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays
%I IGI Global
%P 98-123
%A R. Maheswari
%A P.S. Rao
%A H. Azath
%A V.S. Asirvadam
%D 2023
%O cited By 0
%X The survey on COVID-19 test kits RT-PCR (reverse transcription-polymerase chain reaction) concludes the hit rate of diagnosis and detection is degrading. Manufacturing these RT-PCR kits is very expensive and time-consuming. This work proposed an efficient way for COVID detection using a hybrid convolutional neural network (HCNN) through chest x-rays image analysis. It aids to differentiate non-COVID patient and COVID patients. It makes the medical practitioner to take appropriate treatment and measures. The results outperformed the custom blood and saliva-based RT-PCR test results. A few examinations were carried out over chest X-ray images utilizing ConvNets that produce better accuracy for the recognition of COVID-19. When considering the number of images in the database and the COVID discovery season (testing time = 0.03 s/image), the design reduced the computational expenditure. With mean ROC AUC scores 96.51 & 96.33, the CNN with minimised convolutional and fully connected layers detects COVID-19 images inside the two-class COVID/Normal and COVID/Pneumonia orders. © 2023, IGI Global. All rights reserved.
%L scholars18898