Improved transfer learning based on EMD for Parkinson’s diagnosis

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.