Computer Science

Dual Semi-Supervised Learning for Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Neuropsychological Data

F. D. Lorenzo, A. Antonioni, et al.

Discover how Francesco Di Lorenzo and his team developed a groundbreaking dual semi-supervised learning method to classify Alzheimer's disease, mild cognitive impairment, and normal controls using neuropsychological scores, achieving impressive accuracy that could enhance clinical diagnosis.

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Abstract

This paper proposes a novel dual semi-supervised learning (DSSL) method for classifying Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC) using neuropsychological test scores. The method utilizes difference regularization and consistency regularization with pseudo-labeling, achieving high accuracy (85.47% with 60 labels and 88.40% with 120 labels) and stability compared to other semi-supervised methods on the ADNI database. The study first selected the 15 most relevant features from seven neuropsychological tests using feature selection. DSSL is presented as a valuable tool for clinical diagnosis.

Publisher

Brain Sciences

Published On

Feb 10, 2023

Authors

Francesco Di Lorenzo, Annibale Antonioni, Yan Wang, Xuming Gu, Wenju Hou, Meng Zhao, Li Sun, Chunjie Guo

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