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Longitudinal Brain Structure Changes in Health/MCI Patients: A Deep Learning Approach for the Diagnosis and Prognosis of Alzheimer’s Disease

  • Huawei Technologies Inc., Canada
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... Dai et. al. utilized multilayer perceptron (MLP) for AD diagnosis and prognosis (Dai et al. 2016b). It has to be noted that all the above mentioned work mainly implement a relative 'easy' or 'shallow' version of the deep learning algorithms. ...
Conference Paper
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lzheimer's disease (AD) is a genetically complex neurodegenerative disease, which leads to irreversible brain damage, severe cognitive problems and ultimately death. A number of clinical trials and study initiatives have been set up to investigate AD pathology, leading to large amounts of high dimensional heterogeneous data (biomarkers) for analysis. This paper focuses on combining clinical features from different modalities, including medical imaging, cerebrospinal fluid (CSF), etc., to diagnose AD and predict potential progression. Due to privacy and legal issues involved with clinical research, the study cohort (number of patients) is relatively small, compared to thousands of available biomarkers (predictors). We propose a hybrid pathological analysis model, which integrates manifold learning and Random Vector functional-link network (RVFL) so as to achieve better ability to extract discriminant information with limited training materials. Furthermore, we model (current and future) cognitive healthiness as a regression problem about age. By comparing the difference between predicted age and actual age, we manage to show statistical differences between different pathological stages. Verification tests are conducted based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Extensive comparison is made against different machine learning algorithms, i.e. Support Vector Machine (SVM), Random Forest (RF), Decision Tree and Multilayer Perceptron (MLP). Experimental results show that our proposed algorithm achieves advantageous results than the comparison targets, which indicates promising robustness for practical clinical implementation.
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