Previously submitted to: JMIR Rehabilitation and Assistive Technologies (no longer under consideration since Dec 19, 2025)
Date Submitted: Oct 22, 2025
Open Peer Review Period: Nov 10, 2025 - Jan 5, 2026
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Brain Tissue Classification and Early Detection of Dementia and Alzheimer’s disease Using Machine Learning Algorithms
ABSTRACT
The accurate segmentation of the human brain is essential for early detection of several diseases like Dementia and Alzheimer’s. Accurately pinpointing the anatomical structure of the brain is crucial for precise and dependable diagnostic approaches in various biomedical fields. However, current methods, whether manual or semi-automated, are often time-consuming. Classifying and segmenting of tissues in the brain can shed light on how the tissue contents will vary in order to facilitate early diagnosis. This study aims to implement machine learning algorithms for detection of Dementia and Alzheimer’s diseases by classifying brain tissues using Magnetic Resource Imaging (MRI) images. The proposed research is carried out by using dataset of 300 samples with different attributes, applying various machine learning algorithms like canny edge detection, thresholding, K-Means clustering and Fuzzy C means clustering. The dataset was pre-processed, segmented, and classified using multiple performance metrics. It is observed that Fuzzy C means Clustering demonstrated achieves better performance than edge detection, thresholding, and K-Means clustering techniques in terms of the jaccard index for both white and grey matter. Fuzzy C means Clustering achieves the maximum classification accuracy of 97.82%. This shows the effectiveness of Fuzzy C means Clustering in classifying brain tissue and predicting diseases like Dementia and Alzheimer’s. This study's findings advocate for the Fuzzy C means Clustering algorithm as an auspicious model for classifying the brain tissue and predicting brain diseases like Dementia and Alzheimer’s using MRI images
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