Improved transfer learning based on EMD for Parkinson’s diagnosis

Deyang Sun; Ning Wang; Jinlong Xu; Hao Gao

doi:10.1117/12.2604376

28 October 2021 Improved transfer learning based on EMD for Parkinson’s diagnosis

Deyang Sun, Ning Wang, Jinlong Xu, Hao Gao

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118840V (2021) https://doi.org/10.1117/12.2604376
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

As we known, the life of patients with Parkinson's disease (PD) which cannot be cured fundamentally has changed thoroughly. Then, automatic identification of early Parkinson's disease on feature data sets attracts many medical researchers. At present, machine learning especially deep learning algorithms have been widely adopted in the task of classification and regression, etc. But labeled data sets are rare and expensive to label in many areas, i.e., aerospace, medical. Transfer learning is often employed to solve the problems with small training dataset. In this paper, we proposed a parameters-based transfer learning algorithm to enhance generalization ability and avoid overfitting of the network. Then a new method is utilized to accelerate the training speed of the network, which help the algorithm to achieve results with high speed. At last, the Earth Mover’s Distance (EMD) is introduced into our proposed transfer learning algorithm for enhancing the precise of measurement which represents as a distance metric between the two probability distribution of images. The experimental results compared with other modern algorithms on the common Parkinson’s datasets show the effectiveness of our algorithm.

Citation Download Citation

Deyang Sun, Ning Wang, Jinlong Xu, and Hao Gao "Improved transfer learning based on EMD for Parkinson’s diagnosis", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118840V (28 October 2021); https://doi.org/10.1117/12.2604376

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KEYWORDS

Machine learning

Neural networks

Parkinson's disease

Image processing

Network architectures

Pattern recognition

Visual process modeling

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