An Improved Medical Image Classification Algorithm Based on Adam Optimizer

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

Mathematics, 2024, vol. 12, issue 16, 1-14

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.

Keywords: deep learning; Adam algorithm; gradient cropping; linear interpolation; periodic adjustment of learning rate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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