A Gastrointestinal Image Classification Method Based on Improved Adam Algorithm

Haijing Sun, Jiaqi Cui, Yichuan Shao (), Jiapeng Yang, Lei Xing, Qian Zhao and Le Zhang
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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

Mathematics, 2024, vol. 12, issue 16, 1-13

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.

Keywords: deep learning; Adam algorithm; gastrointestinal image; adaptive gradient clipping; weight decay; computer-aided diagnostic (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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