AUTHOR=Song Xinjian , Gu Feng , Wang Xiude , Ma Songhua , Wang Li TITLE=Interpretable Recognition for Dementia Using Brain Images JOURNAL=Frontiers in Neuroscience VOLUME=15 YEAR=2021 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.748689 DOI=10.3389/fnins.2021.748689 ISSN=1662-453X ABSTRACT=

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. Takagi–Sugeno–Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, 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.