Frontiers in physiology · 2026
Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers.
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Zhen Chaohui, Yao Canhua, Li Song, Lin Zihong, Muhammad Ibrahim Umar, Chipusu Kavimbi, Zheng Biao, Liang Rui
Аннотация
INTRODUCTION: Accurate classification of colonoscopic images is essential for early detection and characterization of colorectal diseases. Recent advances in deep learning, particularly transformer-based architectures and graph neural networks (GNNs), provide alternative strategies for modeling global contextual information and relational structures in image representations. This study evaluates transformer-based and graph-based frameworks under a unified experimental protocol for endoscopic colon disease classification.
METHODS: Experiments were conducted on the Kvasir V2 dataset using two primary paradigms: (i) a Vision Transformer (ViT) with selective fine-tuning and learning-rate scheduling, and (ii) a CNN-GNN pipeline integrating image embeddings with graph construction strategies (cosine similarity, k-nearest neighbors, and epsilon-radius graphs) and multiple GNN architectures. Performance was evaluated using accuracy, precision, recall, and macro-F1 score, with Grad-CAM used for qualitative interpretability analysis.
RESULTS: The selectively fine-tuned Vision Transformer achieved 94.6% accuracy with a macro-F1 score of 0.94. The best graph-based configuration (ViT embeddings with epsilon graph and GIN aggregation) achieved 92% accuracy and 0.92 macro-F1 score.
DISCUSSION: Transformer-based contextual modeling provides strong discriminative capability for image-level colon disease classification, while graph-based relational modeling offers competitive performance when paired with high-quality embeddings.
METHODS: Experiments were conducted on the Kvasir V2 dataset using two primary paradigms: (i) a Vision Transformer (ViT) with selective fine-tuning and learning-rate scheduling, and (ii) a CNN-GNN pipeline integrating image embeddings with graph construction strategies (cosine similarity, k-nearest neighbors, and epsilon-radius graphs) and multiple GNN architectures. Performance was evaluated using accuracy, precision, recall, and macro-F1 score, with Grad-CAM used for qualitative interpretability analysis.
RESULTS: The selectively fine-tuned Vision Transformer achieved 94.6% accuracy with a macro-F1 score of 0.94. The best graph-based configuration (ViT embeddings with epsilon graph and GIN aggregation) achieved 92% accuracy and 0.92 macro-F1 score.
DISCUSSION: Transformer-based contextual modeling provides strong discriminative capability for image-level colon disease classification, while graph-based relational modeling offers competitive performance when paired with high-quality embeddings.