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A Gastrointestinal Image Classification Method Based on Improved Adam Algorithm

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  • Haijing Sun

    (School of Intelligent Science & Engineering, Shenyang University, Shenyang 110044, China)

  • Jiaqi Cui

    (School of Information Engineering, Shenyang University, Shenyang 110044, China)

  • Yichuan Shao

    (School of Intelligent Science & Engineering, Shenyang University, Shenyang 110044, China)

  • Jiapeng Yang

    (School of Information Engineering, Shenyang University, Shenyang 110044, China)

  • Lei Xing

    (School of Chemistry and Chemical Engineering, University of Surrey, Surrey GU2 7XH, UK)

  • Qian Zhao

    (School of Science, Shenyang University of Technology, Shenyang 110044, China)

  • Le Zhang

    (School of Intelligent Science & Engineering, Shenyang University, Shenyang 110044, China)

Abstract

In this study, a gastrointestinal image classification method based on the improved Adam algorithm is proposed. Gastrointestinal image classification is of great significance in the field of medical image analysis, but it presents numerous challenges, including slow convergence, susceptibility to local minima, and the complexity and imbalance of medical image data. Although the Adam algorithm is widely used in stochastic gradient descent, it tends to suffer from overfitting and gradient explosion issues when dealing with complex data. To address these problems, this paper proposes an improved Adam algorithm, AdamW_AGC, which combines the weight decay and Adaptive Gradient Clipping (AGC) strategies. Weight decay is a common regularization technique used to prevent machine learning models from overfitting. Adaptive gradient clipping avoids the gradient explosion problem by restricting the gradient to a suitable range and helps accelerate the convergence of the optimization process. In order to verify the effectiveness of the proposed algorithm, we conducted experiments on the HyperKvasir dataset and validation experiments on the MNIST and CIFAR10 standard datasets. Experimental results on the HyperKvasir dataset demonstrate that the improved algorithm achieved a classification accuracy of 75.8%, compared to 74.2% for the traditional Adam algorithm, representing an improvement of 1.6%. Furthermore, validation experiments on the MNIST and CIFAR10 datasets resulted in classification accuracies of 98.69% and 71.7%, respectively. These results indicate that the AdamW_AGC algorithm has advantages in handling complex, high-dimensional medical image classification tasks, effectively improving both classification accuracy and training stability. This study provides new ideas and expansions for future optimizer research.

Suggested Citation

  • Haijing Sun & Jiaqi Cui & Yichuan Shao & Jiapeng Yang & Lei Xing & Qian Zhao & Le Zhang, 2024. "A Gastrointestinal Image Classification Method Based on Improved Adam Algorithm," Mathematics, MDPI, vol. 12(16), pages 1-13, August.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2452-:d:1451662
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    References listed on IDEAS

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    1. Changqing Wang & Maoxuan Sun & Yuan Cao & Kunyu He & Bei Zhang & Zhonghao Cao & Meng Wang, 2023. "Lightweight Network-Based Surface Defect Detection Method for Steel Plates," Sustainability, MDPI, vol. 15(4), pages 1-12, February.
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