Interpretable Recognition for Dementia Using Brain Images - Song, Xinjian; Gu, Feng; Wang, Xiude; Ma, Songhua; Wang, Li (Frontiers in Neuroscience, 2021)
Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. In this study, we introduce the Takagi–Sugeno–Kang (TSK) fuzzy classifier which is a rule-based model having high interpretability to detect dementia patients caused by Alzheimer’s Disease (AD). To keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.