Hybrid vision transformer and ensemble machine learning framework for automated atherosclerotic plaque classification in intravascular ultrasound imaging.

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Abstract

Atherosclerotic plaque classification in intravascular ultrasound (IVUS) imaging is crucial for cardiovascular disease diagnosis and treatment planning. This paper proposes a novel hybrid framework that combines Vision Transformer (ViT) architecture with traditional machine learning approaches for automated plaque classification. Our three-stage ensemble system leverages the global context understanding capabilities of ViT with the local texture analysis strengths of Gabor filter-based traditional ML methods. The framework consists of: (1) a ViT model with Mixup augmentation for global feature extraction, (2) Gabor filter banks combined with Support Vector Machine (SVM) and Random Forest (RF) classifiers for texture-based analysis, and (3) a weighted ensemble approach that optimally combines predictions from both stages. Evaluated on a comprehensive dataset of 3,867 IVUS images across three plaque categories (Mild Stenotic, Stenotic, Normal Vertebral Artery), our hybrid framework achieves superior performance with 89.39% accuracy and 88.45% F1-score, significantly outperforming individual component models: ViT (79.12%), Gabor-SVM (85.52%), and Gabor-RF (77.78%). Comprehensive comparison with published state-of-the-art methods for IVUS plaque classification demonstrates the effectiveness of our approach, with our hybrid framework achieving superior or competitive performance compared to existing deep learning and traditional ML approaches that establishing new benchmarks for automated IVUS plaque classification. The framework's ability to leverage complementary strengths of transformer-based global learning and traditional texture analysis provides a robust solution for clinical cardiovascular imaging applications.

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