PCA-Enhanced Deep Features for Alzheimer's Disease Stage Classification with EFMM.

Overview

1. Technical Engineering College, Northern Technical University, Mosul 41002, Iraq
2. Technical Engineering College for Computer and Artificial Intelligence, Northern Technical University, Mosul 41002, Iraq; (R.H.A.A.-M.); (M.A.Q.)
3. Mechatronics Engineering Department, College of Engineering, University of Mosul, Mosul 41002, Iraq
4. School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
5. School of Computer Science and Informatics, King’s College London, London WC2R 2LS, UK

Abstract

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder necessitating accurate and timely diagnosis for effective clinical intervention. While deep learning methods have shown promise in AD classification, many rely on computationally intensive architectures and high-dimensional feature representations. This study introduces a lightweight hybrid framework combining deep feature extraction, dimensionality reduction, and adaptive classification for MRI-based Alzheimer’s disease stage classification. Methods: Utilizing MRI images from a publicly available Alzheimer’s disease dataset encompassing four clinical stages (Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented), deep features were extracted using a pre-trained SqueezeNet model as a fixed feature extractor, generating 1000-dimensional feature vectors. Due to the computational complexity and for the improvement of the model efficiency, the dimensionality reduction technique, Principal Component Analysis (PCA) was then applied. This resulted in an optimum representation of 100 principal components, retaining about 96% of the variance. Then, the performances of various machine learning classifiers such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Neural Network (NN), Naïve Bayes (NB), Logistic Regression (LR) and Enhanced Fuzzy Min–Max Neural Network (EFMM) were tested. The accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), and confusion matrices were used to evaluate the performance. Stratified 5-fold cross validation was used to ensure the strength of our results. Results: The findings show that PCA has a significant improvement in classification accuracy for most of the models. In particular, the EFMM classifier outperformed the other classifiers, with an accuracy of 97.19% on the independent test set. After PCA, the AUC values for classes such as Mild Demented, Moderate Demented, Non-Demented and Very Mild Demented were obtained as 97.12%, 99.97%, 93.79% and 95.26% respectively. We further validated our proposed framework using stratified 5-fold cross validation which further corroborated the robustness of our proposed framework. The EFMM achieved a mean accuracy of 98.38% ± 0.36 and a mean macro-F1 score of 98.48% ± 0.43. Friedman statistical testing demonstrated that there were significant differences between the performance of the classifiers evaluated (p < 0.001), which further validated the performance of the EFMM. Conclusions: To sum up, the proposed SqueezeNet–PCA–EFMM is an effective and efficient method for Alzheimer’s disease stage classification under MRI images. The combination of SqueezeNet, PCA, and EFMM—led not only to high classification performance, but also to good cross validation results. Furthermore, this property of incremental learning is the intrinsic one of the EFMM and renders this framework interesting for its incorporation in the next-generation intelligent clinical decision supports in particular, as medical care evolves.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/preetpalsingh25, at Kaggle; found in “Data Availability Statement”

Data Availability Statement

The data supporting the evaluation of the performance of the proposed methods in this study was collected from an online dataset, which can be accessed at the following link: Available online: https://www.kaggle.com/datasets/preetpalsingh25/alzheimers-dataset-4-class-of-images (accessed on 26 July 2025).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 keywords, 1 funder, 37 references.

Cite

This paper

Al-Hatab, M. M. M., Al-Mallah, R. H. A., Qasim, M. A., Mohammed, M. F., Rassem, T. H., & Ahmed, A. A. (2026). PCA-Enhanced Deep Features for Alzheimer's Disease Stage Classification with EFMM. Diagnostics (Basel, Switzerland), 16(15), 2428. https://doi.org/10.3390/diagnostics16152428

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