Paper Contents
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impairs memory and cognitive functions. Early detection is critical for effective treatment and quality of life. This paper presents a deep learning approach using a Convolutional Neural Network (CNN) with LeNet architecture to classify brain MRI scans into four categories: non-demented, very mild, mild, and moderate dementia. The dataset sourced from Kaggle consists of 7679 images, split into training, validation, and test sets. Data preprocessing, normalization, and augmentation were performed to enhance model performance. The model achieved a test accuracy of 56% with a test loss of 0.97. While results show promise, further work is required to improve accuracy and clinical applicability.
Copyright
Copyright © 2025 Sanjana P. This is an open access article distributed under the Creative Commons Attribution License.
