Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network

• Published in: Diagnostics (Basel) Vol. 15; no. 3; p. 377
• Main Authors: Ghadami, Raheleh, Rahebi, Javad
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2025; MDPI
• Subjects: Algorithms; Alzheimer's disease; Brain research; Classification; Clinical outcomes; convolutional neural network (CNN); Dementia; Disease; Diseases; Feature selection; Flavonoids; Harris Hawks Optimization (HHO) algorithm; Hawks; Low income groups; LSTM neural network; Machine learning; magnetic resonance images (MRI); Medical diagnosis; Medical imaging; Medical imaging equipment; Memory; Methods; Neural networks; Neuroimaging; Swarm intelligence; Tissues; magnetic resonance images (MRI); Harris Hawks Optimization (HHO) algorithm; Alzheimer’s disease; convolutional neural network (CNN); LSTM neural network
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics15030377

Background/Objective: Alzheimer’s disease is a progressive brain syndrome causing cognitive decline and, ultimately, death. Early diagnosis is essential for timely medical intervention, with MRI medical imaging serving as a primary diagnostic tool. Machine learning (ML) and deep learning (DL) methods are increasingly utilized to analyze these images, but accurately distinguishing between healthy and diseased states remains a challenge. This study aims to address these limitations by developing an integrated approach combining swarm intelligence with ML and DL techniques for Alzheimer’s disease classification. Method: This proposal methodology involves sourcing Alzheimer’s disease-related MRI images and extracting features using convolutional neural networks (CNNs) and the Gray Level Co-occurrence Matrix (GLCM). The Harris Hawks Optimization (HHO) algorithm is applied to select the most significant features. The selected features are used to train a multi-layer perceptron (MLP) neural network and further processed using a long short-term (LSTM) memory network in order to classify tumors as malignant or benign. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset is utilized for assessment. Results: The proposed method achieved a classification accuracy of 97.59%, sensitivity of 97.41%, and precision of 97.25%, outperforming other models, including VGG16, GLCM, and ResNet-50, in diagnosing Alzheimer’s disease. Conclusions: The results demonstrate the efficacy of the proposed approach in enhancing Alzheimer’s disease diagnosis through improved feature extraction and selection techniques. These findings highlight the potential for advanced ML and DL integration to improve diagnostic tools in medical imaging applications.