Comparative Analysis and Optimization of CNN and RNN Models for Enhanced Viral Disease Detection and Prediction

Asadi Srinivasulu
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Asadi Srinivasulu: Visiting Academic, Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (crcCARE), Global Centre for Environmental Remediation/College of Engineering, Science & Environment, The University of Newcastle, Australia

Biomedical Journal of Scientific & Technical Research, 2024, vol. 59, issue 1, 51067-51085

Abstract: This research examines how effectively Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) can detect and predict viral diseases using an extensive dataset. This dataset includes comprehensive details on 20 different viral infections, such as their symptoms, transmission methods, affected regions, and treatment options. The comparative analysis focuses on various parameters, including accuracy, final training loss, validation loss, time complexity, interpretability, convergence speed, overfitting risk, robustness, and generalization.

Keywords: Journals on Medical Drug and Therapeutics; Journals on Emergency Medicine; Physical Medicine and Rehabilitation; Journals on Infectious Diseases Addiction Science and Clinical Pathology; Open Access Clinical and Medical Journal; Journals on Biomedical Science; List of Open Access Medical Journal; Journals on Biomedical Engineering; Open Access Medical Journal; Biomedical Science Articles; Journal of Scientific and Technical Research (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:abf:journl:v:59:y:2024:i:1:p:51067-51085

DOI: 10.26717/BJSTR.2024.59.009236

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