Alzheimer’s disease (AD) is a progressive and irreversible disease which gradually makes patients unable to do their daily routines. Although the present treatments can not cure the disease completely, its early detection can reduce symptoms and enhance the patients’ li More
Alzheimer’s disease (AD) is a progressive and irreversible disease which gradually makes patients unable to do their daily routines. Although the present treatments can not cure the disease completely, its early detection can reduce symptoms and enhance the patients’ life quality. In the current literature, using the grey matter (GM) tissue which is known as an appropriate biomarker is highly common in AD diagnosis. However, two other brain tissues known as cerebrospinal fluid (CSF) and white matter (WM) seem to reveal beneficial information about the patients’ brain changes. The aim of the present study is to develop an automatic system for the early diagnosis of Alzheimer’s disease from structural MRI by simultaneously considering suitable features of all GM, CSF and WM tissues. A SVM-RBF classifier is trained and evaluated on the OASIS database to separate AD from healthy control (HC) subjects. The obtained results represent higher accuracy and sensitivity of the proposed algorithm in comparison with similar method.
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