Hybrid CNN and SVM model for Alzheimer’s disease classification using categorical focal loss function
Wided Hechkel, Rim Missaoui, Abdelhamid Helali, Marco Leo
Abstract
• Novel Hybrid Model: Proposes a CNN-SVM architecture enhanced by categorical focal loss (CFL) to address class imbalance in Alzheimer’s disease (AD) stage classification. • Data Imbalance Mitigation: Integrates CFL to improve model robustness against uneven training data distributions. • Two-Stage Classification: Combines CNN-based feature extraction from MRI scans with SVM classification using an RBF kernel to preserve complex data relationships. • State-of-the-Art Performance: Achieves 98.52 % accuracy on the kaggle dataset, outperforming existing methods. • Clinical Relevance: Enables precise categorization of AD into four stages (non-demented to moderately demented), aiding early diagnosis and personalized treatment strategies. Alzheimer’s disease (AD) is the leading cause of dementia worldwide. It attacks the elderly population, causing a dangerous cognitive decline and memory loss due to the degeneration and atrophy of brain neurons. Recent developments in machine learning techniques for the detection and classification of AD boost the early diagnosis and enable slowing the disease by adopting preclinical treatments. However, a major defect of these techniques is their high complexity architectures and their less generalizability, which provokes difficulties in clinical integration. This paper presents a new approach that combines convolutional neural network (CNN) and support vector machines (SVM) for the detection of AD. CNN stage enhances the accuracy of the system because it is an excellent feature extractor. SVM stage handles classification performance by optimizing the decision boundaries; meanwhile, it requires fewer hyperparameter updates compared to end-to-end CNN with Softmax classifier. SVM reduces the computational cost of the training. Experiments are conducted on the Kaggle dataset for Magnetic Resonance Imaging (MRI) brain images of AD. The hybrid model achieved accuracy scores of 98.52 %, 97.71 %, and 97.58 % for the training set, validation set, and testing set respectively, inference times per sample of 0.0588s, 0.0586s, and 0.0592s on the above three sets respectively. Obtained results confirm high effectiveness and potential prospect of the developed CNN-SVM model in early diagnosis of AD with reduced implementation complexity.