Received June 3, 2021, accepted June 14, 2021, date of publication June 18, 2021, date of current version June 30, 2021. Digital Object Identifier 10.1109/ACCESS.2021.3090474 DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images SURIYA MURUGAN 1, CHANDRAN VENKATESAN 2, M. G. SUMITHRA 2, (Senior Member, IEEE), XIAO-ZHI GAO 3, B. ELAKKIYA 4, M. AKILA 5, AND S. MANOHARAN 6 1Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India 2Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India 3School of Computing, University of Eastern Finland, 70210 Kuopio, Finland 4Department of Electronics and Communication Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India 5Department of Computer Science Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India 6Department of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Ambo University, Ambo 00251, Ethiopia Corresponding author: S. Manoharan (
[email protected]) ABSTRACT Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer's disease characteristics from MRI images.