Early diagnosis of Alzheimer's disease from MRI images with deep learning model
This work addresses early diagnosis of Alzheimer's disease for patients and clinicians, but it is incremental as it applies existing methods (SMOTE and pre-trained CNN) to a specific dataset.
The paper tackled early diagnosis of Alzheimer's disease from MRI images using a deep learning model, achieving an accuracy of 98.67% by addressing class imbalance with SMOTE and applying a pre-trained CNN.
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%.