Ch.V Gopikrishna, D. Nagesh, P. Nithin Goud, S. Deepak, T. Shashi 06 03 Alzheimers disease a leading cause of dementia is characterized by memory loss and neurodegenerative disorders Early diagnosis plays a crucial role in enhancing patient care and treatment outcomes Traditional approaches for Alzheimers disease diagnosis have limitations in terms of efficiency and learning time Deep learningbased approaches particularly Convolutional Neural Networks CNNs have shown promise in the classification of neuroimaging data related to Alzheimers disease In this presentation we explore the use of a 12layer CNN model trained on our datasets for early detection of Alzheimers disease Experimental results highlight the effectiveness of our proposed approach in improving accuracy and efficiency in Alzheimers disease detection Our research aims to contribute to the advancement of diagnostic techniques for Alzheimers disease through the application of deep learning algorithms Alzheimers disease AD is a neurodegenerative disease and the most common cause of dementia in older adults The part of brain that gets affected in this disease is hippocampus degeneration Detection of Alzheimers disease at preliminary stage is very important as it can prevent serious damage to the patients brain It becomes dangerous and sometimes fatal in case of people of 65 years of age or above The main objective of this project is to use machine learning algorithms that is and feature extraction and selection to predict the Alzheimers disease and build a useful model The dataset is taken in the form of images The proposed approach detects the Alzheimers Disease such as moderatedemented and non demented using CNN algorithm Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026