Research Article
Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images
@INPROCEEDINGS{10.1007/978-3-031-77075-3_16, author={Yallapu Srinivas and M. Aravind Kumar}, title={Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images}, proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I}, proceedings_a={IC4S}, year={2025}, month={2}, keywords={Convolutional Neural Network AlexNet VGGNet ResNet Inception Deep learning EfficientNet RegNet ViT (Vision Transformer) Swin Transformer}, doi={10.1007/978-3-031-77075-3_16} }- Yallapu Srinivas
M. Aravind Kumar
Year: 2025
Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images
IC4S
Springer
DOI: 10.1007/978-3-031-77075-3_16
Abstract
Computer vision and image classification have been used significantly in the clinical field, due to the availability and implementation of various Convolutional Neural Networks (CNNs) over the past decade. Hence, we present an analysis report on several prominent CNN architectures such as AlexNet, VGGNet, Inception (GoogLeNet), ResNet, EfficientNet, RegNet, ViT (Vision Transformer), and Swin Transformer by exploring their historical context, architectural details, and key innovations. Finally, we aim to assist researchers and practitioners in choosing the most appropriate architecture by comparing the accuracy, trainable parameters, and computational requirements of aforementioned architectures to identify COVID-19 from chest X-ray images for further clinical process/specific research.
