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Hybrid Deep Learning Models for Effective COVID -19 Diagnosis with Chest X-Rays
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Author(s): Maheswari R. (Vellore Institute of Technology, India), Prasanna Sundar Rao (Shri Sankarlal Sundarbai Shasun Jain College, India), Azath H. (Vellore Institute of Technology, Bhopal, India) and Vijanth S. Asirvadam (Universiti Teknologi Petronas, Malatysia)
Copyright: 2023
Pages: 26
Source title:
Structural and Functional Aspects of Biocomputing Systems for Data Processing
Source Author(s)/Editor(s): U. Vignesh (Vellore Institute of Technology, Chennai, India), R. Parvathi (Vellore Institute of Technology, India) and Ricardo Goncalves (Department of Electrical and Computer Engineering (DEEC), NOVA School of Science and Technology, NOVA University Lisbon, Portugal)
DOI: 10.4018/978-1-6684-6523-3.ch005
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Abstract
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.
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