Abstract
Recent innovations in technology related to medical fields are widely wished to enhance prevention, diagnosis, and treatment. Seen that Alzheimer's Disease (AD) is hard to be identified at an earlier stage, many approaches and techniques are proposed. Detecting the AD-based in Magnetic Resonance Images (MRI) presents a great challenge. The recognition of AD helps to slow the effects of the disease when using an early treatment. An automated tool called Computer-Aided Diagnosis (CAD) is well invited to recognize and to identify AD. The motivation behind this work is to assess the features of how much explicit highlights the AD. The Gyrification index, the cortical thickness, and the Alzheimer's Disease Assessment Scale (ADAS) are studied in this paper. Many classifiers are implemented to highlight the best one. In this paper, we propose to use the classifier Data-driven Error Correcting Output Code (DECOC) prepared with Gyrification index, cortical thickness, and ADAS psychological grades. The proposed CAD framework identifies AD more accurately than others done by alternative classifiers. The outcomes prove that the cortical thickness and the ADAS psychological grades provide accurate identification of the AD instead of other features.