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@article{207237,
        author = {Dr L Subathra Devi and Dr K Devipriya},
        title = {MRI-Based Alzheimer's Disease Classification Using 3D CNN, SE Attention & Support Vector Machine},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {172-182},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207237},
        abstract = {Background/Objective: The main objective is the Earlier detection of Alzheimer's disease using deep learning model Alzheimer disease is a fatal progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Methods/Statistical Analysis: The proposed methodology includes two methods. First, proposed connected median filter using PSO feature extraction from MRI images and Analysis of Alzheimer’s diseases state by using 3D-CNN based SE-Net. In the first phase, the algorithm first normalizes and removes the skull from the MRI images. Connected median filter using Particle Swarm Optimization algorithm is used to partition the image into white matter (WM), grey matter (GM) and black holes (BH). Findings: The relevant diagnostic features are extracted from the segmented image component. The classifier is trained by the training data to predict the test data. The features are defined to construct classification models by using Support Vector Machine with Squeeze- Excitation block. Novelty /Improvements: Deep Learning demands a large number of images and its strength was increased as per requirement by augmentation technique. In this phase, 1000 images of different features are selected to train the SVM classifier and the accuracy is improved by 98.37% when compared with VGG-16 Net based CNN model and Dilated 3D-CNN model.},
        keywords = {Deep Learning, Alzheimer disease detection, SVM hybrid Classifier, 3D- CNN based SE - NET},
        month = {August},
        }