Author
Listed:
- Haijing Sun
(School of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China)
- Wen Zhou
(School of Information Engineering, Shenyang University, Shenyang 110044, China)
- Jiapeng Yang
(School of Information Engineering, Shenyang University, Shenyang 110044, China)
- Yichuan Shao
(School of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China)
- Lei Xing
(School of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, UK)
- Qian Zhao
(School of Science, Shenyang University of Technology, Shenyang 110044, China)
- Le Zhang
(School of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China)
Abstract
Due to the complexity and illegibility of medical images, it brings inconvenience and difficulty to the diagnosis of medical personnel. To address these issues, an optimization algorithm called GSL(Gradient sine linear) based on Adam algorithm improvement is proposed in this paper, which introduces gradient pruning strategy, periodic adjustment of learning rate, and linear interpolation strategy. The gradient trimming technique can scale the gradient to prevent gradient explosion, while the periodic adjustment of the learning rate and linear interpolation strategy adjusts the learning rate according to the characteristics of the sinusoidal function, accelerating the convergence while reducing the drastic parameter fluctuations, improving the efficiency and stability of training. The experimental results show that compared to the classic Adam algorithm, this algorithm can demonstrate better classification accuracy, the GSL algorithm achieves an accuracy of 78% and 75.2% on the MobileNetV2 network and ShuffleNetV2 network under the Gastroenterology dataset; and on the MobileNetV2 network and ShuffleNetV2 network under the Glaucoma dataset, an accuracy of 84.72% and 83.12%. The GSL optimizer achieved significant performance improvement on various neural network structures and datasets, proving its effectiveness and practicality in the field of deep learning, and also providing new ideas and methods for solving the difficulties in medical image recognition.
Suggested Citation
Haijing Sun & Wen Zhou & Jiapeng Yang & Yichuan Shao & Lei Xing & Qian Zhao & Le Zhang, 2024.
"An Improved Medical Image Classification Algorithm Based on Adam Optimizer,"
Mathematics, MDPI, vol. 12(16), pages 1-14, August.
Handle:
RePEc:gam:jmathe:v:12:y:2024:i:16:p:2509-:d:1456117
Download full text from publisher
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:2509-:d:1456117. 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.
We have no bibliographic references for this item. You can help adding them by using 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.