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—Cyclic, Step Based, Time-Based, and Epoch
Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The
performance 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.
References
[1]
World Health Organization (2024) Coronavirus.
https://www.who.int/health-topics/coronavirus#tab=tab_1
[2]
Cleveland Clinic (2024) COVID-19 and PCR Testing.
https://my.clevelandclinic.org/health/diagnostics/21462-covid-19-and-pcr-testing
[3]
Tahir, H., Iftikhar, A. and Mumraiz, M. (2021) Forecasting COVID-19 via Registration Slips of Patients Using ResNet-101 and Performance Analysis and Comparison of Prediction for COVID-19 Using Faster R-CNN, Mask R-CNN, and ResNet-50. 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, 19-20 February 2021, 1-6.
https://doi.org/10.1109/ICAECT49130.2021.9392487
[4]
Zhang, J., et al. (2023) Graph Convolution and Self-Attention Enhanced CNN with Domain Adaptation for Multi-Site COVID-19 Diagnosis. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, 24-27 July 2023, 1-4. https://doi.org/10.1109/EMBC40787.2023.10340851
[5]
Dandıl, E. and Yıldırım, M. S. (2022) Automatic Segmentation of COVID-19 Infection on Lung CT Scans Using Mask R-CNN. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, 9-11 June 2022, 1-5. https://doi.org/10.1109/HORA55278.2022.9800029
[6]
Khadija, B. (2022) Automatic Detection of Covid-19 Using CNN Model Combined with Firefly Algorithm. 2022 8th International Conference on Optimization and Applications (ICOA), Genoa, 6-7 October 2022, 1-4,
https://doi.org/10.1109/ICOA55659.2022.9934144
[7]
Arul Raj, A.M. and Sugumar, R. (2023) Enhancing COVID-19 Diagnosis with Automated Reporting Using Preprocessed Chest X-Ray Image Analysis Based on CNN. 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, 4-6 May 2023, 35-40.
https://doi.org/10.1109/ICAAIC56838.2023.10141515
[8]
Ul Haq, A., et al. (2021) Deep Learning Approach for COVID-19 Identification. 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 17-19 December 2021, 154-156. https://doi.org/10.1109/ICCWAMTIP53232.2021.9674079
[9]
Marusani, J., Sudha, B.G. and Darapaneni, N. (2022) Small-Scale CNN-N Model for Covid-19 Anomaly Detection and Localization From Chest X-Rays. 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR), Hyderabad, 10-12 March 2022, 1-6.
https://doi.org/10.1109/ICAITPR51569.2022.9844184
[10]
Hammad, H. and Khotanlou, H. (2022) Detection and Visualization of COVID-19 in Chest X-Ray Images Using CNN and Grad-CAM (GCCN). 2022 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Bam, 2-4 March 2022, 1-5.
https://doi.org/10.1109/CFIS54774.2022.9756420
[11]
Prasad, K.S., Pasupathy, S., Chinnasamy, P. and Kalaiarasi, A. (2022) An Approach to Detect COVID-19 Disease from CT Scan Images Using CNN—VGG16 Model. 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 25-27 January 2022, 1-5.
https://doi.org/10.1109/ICCCI54379.2022.9741050
[12]
Raikote, P. (2020) Covid-19 Image Dataset.
https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset/