Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{187851,
        author = {Gurrala Sravani and Chiranjeevi Althi and Dr. T. Ramashri},
        title = {Hybrid PCA–ACO–SVM Approach for Multi-Stage Classification of Alzheimer’s Disease using Brain MRI Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7417-7422},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187851},
        abstract = {Alzheimer (AD) is a neurological complaint of the brain that impacts primarily on thinking capacities and makes one forget. This is a common disease in the number of people today and it is important to determine the level of AD. This suggested architecture divides the brain MRI data into four phases using a variety of machine learning techniques. The framework begins with a preprocessing method to improve MRI image clarity, then performs Principal Component Analysis (PCA) to reduce dimensionality, then selects the most informative feature using Ant Colony Optimization (ACO), and finally classifies the selected features with Support Vector Machine (SVM) using the RBF kernel for non-linear classification. To evaluate the system's performance, metrics including recall, accuracy, precision, and F1-score are employed. The confusion matrix and chosen feature mask representations are also included in the evaluation. As a result, the simulation results were 98.52% accurate, outperforming earlier methods based on machine learning.},
        keywords = {ACO, Four Stages of Alzheimer's disease, machine learning, MRI images, PCA, SVM.},
        month = {December},
        }