Parkinson's image detection and classification based on deep learning.

Journal: BMC medical imaging
Published Date: Jul 25, 2024

Abstract

OBJECTIVE: There are two major issues in the MRI image diagnosis task for Parkinson's disease. Firstly, there are slight differences in MRI images between healthy individuals and Parkinson's patients, and the medical field has not yet established precise lesion localization standards, which poses a huge challenge for the effective prediction of Parkinson's disease through MRI images. Secondly, the early diagnosis of Parkinson's disease traditionally relies on the subjective judgment of doctors, which leads to insufficient accuracy and consistency. This article proposes an improved YOLOv5 detection algorithm based on deep learning for predicting and classifying Parkinson's images.

Authors

  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Zixuan Yang
    Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Weimin Qi
    Department of Neurology, General Hospital of Ningxia Medical, Ningxia, 750004, China.
  • Xinchen Yu
    Department of Computer Engineering, Jiangsuiangsu Ocean University, Lianyungang, 222005, China.
  • Jiaying Wu
    Department of Computer Engineering, Jiangsuiangsu Ocean University, Lianyungang, 222005, China. jiaying@jou.edu.cn.
  • Haining Li
    Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory, Yinchuan, China.

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