论文标题
可解释的机器学习模型，用于使用石灰在DATSCAN图像上早期检测帕金森氏病
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery
论文作者
论文摘要
帕金森氏病（PD）是一种退化性和进行性神经系统状况。早期诊断可以改善患者的治疗方法，并通过SPECT DATSCAN等多巴胺能成像技术进行。在这项研究中，我们提出了一种机器学习模型，该模型将任何给定的Datscan准确地归类为患有帕金森氏病，除了为预测提供了合理的理由外。这种推理是通过使用使用局部可解释的模型 - 静态解释器（LIME）方法生成的视觉指标来完成的。 DatScans是从帕金森的进步标记计划数据库中汲取的，并使用转移学习对CNN（VGG16）进行了培训，其精度为95.2％，灵敏度为97.5％，特异性为90.9％。将模型对重要性的解释性保释性，尤其是在医疗保健领域，本研究利用Lime解释将PD与非PD区分开，并使用DATSCANS上的Visual Superpixels。可以得出结论，拟议的系统与其可解释性和准确性结合，可以有效地帮助医务人员早期诊断帕金森氏病。
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 is 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.
