Early diagnosis of Alzheimer's disease from MRI images with deep learning model

TL;DR

This paper presents a deep learning framework using CNNs and SMOTE to accurately diagnose Alzheimer's disease from MRI images, addressing class imbalance and achieving high accuracy.

Contribution

It introduces a novel combination of pre-trained CNNs and SMOTE for early AD diagnosis from MRI scans, improving classification performance.

Findings

Achieved 98.67% accuracy in AD classification.

Effectively handled class imbalance with SMOTE.

Demonstrated the effectiveness of pre-trained CNNs for feature extraction.

Abstract

It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%.