Parkinson's Disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTSCAN. In this study, we propose a machine learning model that accurately classifies any given DaTSCAN as having Parkinson's disease or not, in addition to providing a plausible reason for the prediction. This kind of reasoning is done through the use of visual indicators generated using Local Interpretable Model-Agnostic Explainer (LIME) methods. DaTSCANs were drawn from the Parkinson's Progression Markers Initiative database and trained on a CNN (VGG16) using transfer learning, yielding an accuracy of 95.2%, a sensitivity of 97.5%, and a specificity of 90.9%. Keeping model interpretability of paramount importance, especially in the healthcare field, this study utilises LIME explanations to distinguish PD from non-PD, using visual superpixels on the DaTSCANs. It could be concluded that the proposed system, in union with its measured interpretability and accuracy may effectively aid medical workers in the early diagnosis of Parkinson's Disease.

帕金森氏病（PD）是一种退化性和进行性神经系统疾病。早期诊断可以改善对患者的治疗，并且可以通过像SPECT DaTSCAN这样的多巴胺能成像技术进行诊断。在这项研究中，我们提出了一种机器学习模型，除了为预测提供合理的理由外，该模型还可以将任何给定的DaTSCAN准确分类为是否患有帕金森氏病。这种推理是通过使用使用局部可解释模型不可知解释器（LIME）方法生成的视觉指示器来完成的。DaTSCANs是从帕金森氏症进展指标计划数据库中提取的，并使用转移学习在CNN（VGG16）上进行了训练，其准确度为95.2％，灵敏度为97.5％，特异性为90.9％。保持模型的可解释性至为重要，尤其是在医疗保健领域，本研究利用DaTSCAN上的视觉超像素，利用LIME解释来区分PD与非PD。可以得出结论，所建议的系统结合其可测量的可解释性和准确性，可以有效地帮助医务人员早期诊断帕金森氏病。