基于深度学习的帕金森病磁共振图像辅助诊断

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中图分类号：TP391文献标志码：A

Abstract： Parkinson's disease is a prevalent neurodegenerative disorder characterized by insidious onset， complex symptoms，and the accuracy of clinical diagnosis highly depends on the doctor's experience， lacking objective and quantitative diagnostic techniques. With the advancement of artificial intelligence， the integration of deep learning techniques holds promise as an accurate and efficient approach for automated PD diagnosis. This paper proposes a deep learning-based framework for assisting in the diagnosis of Parkinson's disease.Firstly，an image segmentation model is employed to segment the midbrain region of axial slices of the brain. Secondly，the MobilenetV2 network was improved by incorporating inception architecture， CA attention mechanism，and TanhExp activation function. The improved MobilenetV2 model was trained and tested using midbrain images， achieving a diagnostic accuracy of 97.5% ，sensitivity of 97.53% ，and recall of 97.48% in distinguishing PD cases from normal controls.The performance of the model not only surpasses that of other classical networks but also more focuses on characteristic regions relevant to Parkinson's pathology， thereby providing accurate and reliable diagnostic outcomes.

Keywords： Parkinson's disease; magnetic resonance imaging; image segmentation; MobilenetV2; image classification

引言

帕金森病（Parkinson'sdisease，PD）又称震颤麻痹，迄今为止仅可延缓其病程，尚无治愈方法。（剩余13472字）