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The main goal of this research is to use cutting-edge artificial intelligence (AI) approaches to improve the accuracy of virus segmentation and classification in microscopic pictures. Convolutional Neural Network (CNN) is used in the study as the baseline method, and its performance is compared to that of the ResNet152V2 algorithm. Supplies and Procedures: The 8002 microscopic image recordings in the dataset were used to train and assess the CNN and ResNet152V2 models. Forty iterations of CNN and ResNet152V2 with a sample size of 20 were used in the experiment to predict the accuracy of segmentation and classification.

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AI-Driven Virus Segmentation and Classification Unveiling the Microscopic Images Based on CNN Comparing with ResNet152V2 Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Krishnakumar Mithun Ram,
  • Paranthaman Vijayan

摘要

The main goal of this research is to use cutting-edge artificial intelligence (AI) approaches to improve the accuracy of virus segmentation and classification in microscopic pictures. Convolutional Neural Network (CNN) is used in the study as the baseline method, and its performance is compared to that of the ResNet152V2 algorithm. Supplies and Procedures: The 8002 microscopic image recordings in the dataset were used to train and assess the CNN and ResNet152V2 models. Forty iterations of CNN and ResNet152V2 with a sample size of 20 were used in the experiment to predict the accuracy of segmentation and classification.