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뇌의 회백질 두께를 이용한 다발성경화증과 시신경척수염범주질환 분류 모델 개발
오지석(JiSeok Oh),조은빈(Eunbin Cho),민주홍(JuHong Min),한철(Cheol E. Han) 대한전자공학회 2021 대한전자공학회 학술대회 Vol.2021 No.6
Both multiple sclerosis (MS) and neuromyelitis optica spectrum disease (NMOSD) deteriorates neural fibers in the white matter. However, they are fundamentally different disease, and thus require distinct medical treatments. In this paper, we investigate the efficacy of the machine learning model to classify MS and NMOSD only based on gray-matter characteristics, cortical thickness, extracted from the widely used T1-weighted MR images. We employed a simple multi-layer perceptron (MLP) and support vector machine (SVM). We also compared a 2-step binary classification model and 1-step multi-class classification model. We found that the former out-performed the latter. We achieved the accuracy of 83% on average.