Work

An integrated machine learning based adaptive error minimization framework for Alzheimer's stage identification

Year: 2025

Type: article

Abstract: Alzheimer's disease (AD) is a degenerative neurological condition that impairs cognitive functioning. Early detection is critical for slowing disease progression and limiting brain damage. Although machine learning and deep learning models help identify Alzheimer's disease, their accuracy and efficiency are widely questioned. This study provides an integrated system for classifying four AD phases from 6400 MRI scans using pre-trained neural networks and machine learning classifiers. Preprocessing steps include noise removal, image enhancement (AGCWD, Bilateral Filter), and segmentation. Intensity normalization and data augmentation methods are applied to improve model generalization. Two models are developed: the first employs pre-trained neural net-works (VGG16, VGG19, DenseNet201, ResNet50, EfficientNetV7, InceptionV3, InceptionResNetV2, and MobileNet) for both feature extraction and classification. In contrast, the second integrates features from these networks with machine learning classifiers (XGBoost, Random Forest, SVM, KNN, Gradient Boosting, AdaBoost, Decision Tree, Linear Discriminant Analysis, Logistic Regression, and Multilayer Perceptron). The second model incorporates an adaptive error minimization sys-tem for enhanced accuracy. VGG16 achieved the highest accuracy (99.61 % training and 97.94 % testing), whereas VGG19+MLP with adaptive error minimization achieved 97.08 %, exhibiting superior AD classification ability. • Combines pre-trained deep learning models with machine learning classifiers for Alzheimer's stage classification. • Introduces an error minimization system to enhance classification accuracy over multiple iterations. • Utilizes Bilateral Filtering, Adaptive Gamma Correction, and segmentation for high-quality image input. • Implements data augmentation and resampling techniques to improve model generalization. • Achieves 97.08 % accuracy, outperforming state-of-the-art Alzheimer's classification models.

Source: Intelligence-Based Medicine

DOI: 10.1016/j.ibmed.2025.100243

Authors: Fahima Hossain, Rajib Kumar Halder, Mohammed Nasir Uddin

Institution: Jagannath University

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,420