STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Neuroscience Original Research

Deep Learning-Based Alzheimer’s Disease Detection: A CNN Approach

  1. Malik Hyder Ali Department Computer Science and Engineering Akal University
  2. Harsh Kumar Department of Computer Science and Engineering

Abstract

Alzheimer’s disease (AD) is a neurological condition that worsens with time and impairs a patient’s quality of life by causing cognitive loss. For prompt intervention and management of AD, early identification is essential. In this work, we propose a deep learning-based method for automatically classifying Alzheimer’s disease from medical imaging data using convolutional neural networks (CNNs). Our algorithm is intended to evaluate brain MRI images and detect anatomical variations suggestive of AD. The CNN architecture is designed to discriminate between people who are healthy, those who have mild cognitive impairment, and people who have Alzheimer’s disease with high accuracy and robustness. The model demonstrated noteworthy performance in terms of accuracy, sensitivity, and specificity when it was trained on a dataset of MRI scans. According to our findings, CNN-based models present a viable means of diagnosing Alzheimer’s disease early and precisely, which can help doctors decide on the best course of treatment. This work demonstrates the promise of deep learning for neuroimaging and its use in diagnosing neurodegenerative diseases.

Keywords

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