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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.

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Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images

  • 会议论文

  • 发表日期：

    2025年

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  • Yallapu Srinivas,
  • M. Aravind Kumar

摘要

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.