%0 Journal Article
%T Advancing COVID-19 Diagnosis with CNNs: An Empirical Study of Learning Rates and Optimization Strategies
%A Mainak Mitra
%A Soumit Roy
%J Intelligent Control and Automation
%P 45-78
%@ 2153-0661
%D 2023
%I Scientific Research Publishing
%R 10.4236/ica.2023.144004
%X The rapid spread of the novel Coronavirus (COVID-19) has emphasized the
necessity for advanced diagnostic tools to enhance the detection and management
of the virus. This study investigates the effectiveness of Convolutional Neural
Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images,
focusing on the impact of varying learning rates and optimization strategies.
Despite the abundance of chest X-ray datasets from various institutions, the
lack of a dedicated COVID-19 dataset for computational analysis presents a
significant challenge. Our work introduces an empirical analysis across four
distinct learning rate policies¡ªCyclic, Step Based, Time-Based, and Epoch
Based¡ªeach tested with four different optimizers: <span>Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The
performance </span>of these configurations was evaluated in terms of training
and validation accuracy over 100 epochs. Our results demonstrate significant
differences in model performance, with the Cyclic learning rate policy combined
with SGD optimizer achieving the highest validation accuracy of 83.33%. This
study contributes to the existing body of knowledge by outlining effective CNN
configurations for COVID-19 image dataset analysis, offering insights into the
optimization of machine learning models for the diagnosis of infectious
diseases. Our findings underscore the potential of CNNs in supplementing
traditional PCR tests, providing a computational approach to identify patterns
in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the
swift and accurate diagnosis of the virus.
%K Learning Rate
%K AI
%K Optimizer
%K Deep Learning
%K CNN
%K Multi Class Classification
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=132114