Mahbub, RiasatAzim, Muhammad AnwarulMahee, Nafiz IshtiaqueSanjid, Zahidul IslamReza, Khondaker MasfiqParvez, Mohammad Zavid2026-08-272026-08-272021-01-01R. Mahbub, M. A. Azim, N. I. Mahee, Z. I. Sanjid, K. M. Reza and M. Z. Parvez, "Neural Network Architecture for the Classification of Alzheimer's Disease from Brain MRI," TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Auckland, New Zealand, 2021, pp. 693-697, doi: 10.1109/TENCON54134.2021.9707412.9781665495325215934422-s2.0-85125964616https://hdl.handle.net/10361/29567Alzheimer's Disease (AD) is a neurological condition in which the decline of brain cells causes memory loss and cognitive decline. Various Neuroimaging techniques have been developed to diagnose AD; among those, Magnetic Resonance Imaging (MRI) is one of the most prominent ones. Historically, expert radiologists were solely responsible for making decisions of a patient's AD situation by manually analyzing brain MR images. However, the recent progress in medical image analysis using deep learning especially has automated this task significantly. Although the state-of-The-Art architectures have achieved human-level performance in classifying AD images from Normal Control (NC), they often require predefined Regions of interest as a basis for feature extraction. This condition not only requires specialized domain knowledge of the human brain but also makes the overall design complicated. In this paper, we designed a 14 layer Neural network architecture that can facilitate AD diagnosis without being dependent on any neurological assumption. The network was tested over ADNI-1, a benchmark MRI dataset for AD research, and found an accuracy of 87.06 % (\mathbf{AUC}=\mathbf{0. 9 3}.693-697en-USAlzheimer's diseaseConvolutional neural networkMagnetic resonance imagingAlzheimer's disease.Magnetic resonance imaging.Conference Proceeding10.1109/TENCON54134.2021.9707412