Alzheimer Classifications Combining Machine Learning and Signal Processing
Samsi Ara1, Md. Imdadul Islam2, Jugal Krishna Das3, Md. Golam Saklayen4, Md. Mizanur Rahman5
1Samsi Ara, Department of Computer Science & Engineering, Jahangirnagar University, Savar (Dhaka), Bangladesh.
2Md. Imdadul Islam, Department of Computer Science & Engineering, Jahangirnagar University, Savar (Dhaka), Bangladesh.
3Jugal Krishna Das, Department of Computer Science & Engineering, Jahangirnagar University, Savar (Dhaka), Bangladesh.
4Md. Golam Saklayen, Department of Applied Physics and Electronic Engineering, Rajshahi University, Rajshah (Dhaka), Bangladesh.
5Md. Mizanur Rahman, Department of Applied Physics and Electronic Engineering, Rajshahi University, Rajshahi (Dhaka), Bangladesh.
Manuscript received on 23 May 2025 | First Revised Manuscript received on 30 May 2025 | Second Revised Manuscript received on 17 September 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025 | PP: 18-29 | Volume-15 Issue-1, October 2025 | Retrieval Number: 100.1/ijeat.E467314050625 | DOI: 10.35940/ijeat.E4673.15011025
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Manual detection of presence of Alzheimer even determination of its intensity from Magnetic Resonance Imaging (MRI) image is an easy task when a patient is heavily affected by the disease. The situation becomes cumbersome for a physician when a very mild affected image comes under consideration. For cases involving subtle differences in images, Machine Learning (ML) and Deep Learning (DL) classification are considered the best solutions. In this paper, Alzheimer-affected images of four categories —mild impairment, moderate impairment, very mild impairment, and no impairment — are taken from a benchmark open database. The image set is converted to a numerical feature vector using Speeded-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Harris–Stephen’s, and minimum eigenvalue. The feature vector is then applied to several ML algorithms: Fuzzy Inference System (FIS), Multiple Linear Regression (MLR), Fuzzy C-Means (FCM), Naïve Bayes and linear Support Vector Machine (SVM) to check their eligibility in classifying these four types of medical images. The accuracy of detection from any individual method ranges from 56% to 68%. However, applying the maximum voting scheme to all the MLs yields an accuracy of 75%. The long feature vector is also extracted from the discrete wavelet transform (DWT), and classification is done from its coherence, but the accuracy is found to be very poor. Next, the image dataset is applied to the Bag of Features (BoF) algorithm using 500 visual words, yielding a moderate result with an accuracy of 84.28%. Finally, two deep learning models are applied: PyTorch Convolutional Neural Network (CNN) and Keras Visual Geometry Group (VGG) 19. These models are tested on 20% of the trained images, achieving accuracies of 95.7% and 97.1%, respectively.
Keywords: BoF, Accuracy, Coherence of DWT, FIS, and Parallel Plot.
Scope of the Article: Artificial Intelligence and Methods