Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays

• Main Authors: Maheswari, R., Rao, P.S., Azath, H., Asirvadam, V.S.
• Format: Book
• Published: IGI Global 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/37684/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151470195&doi=10.4018%2f978-1-6684-6523-3.ch005&partnerID=40&md5=bbb4235a7e39fd4b54ba55651cb702c2

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.