Author
Listed:
- 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
Download full text from publisher
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2452-:d:1451662. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.