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Architecture of the <t>CNN-GNN</t> pipeline for colon disease classification. The presentation of a detailed, step-by-step breakdown of the CNN-GNN pipeline, with each stage visually represented, highlighting the transition from raw medical images to classification outputs. <xref ref-type=Figure 5 illustrates the stages involved in analyzing a colonoscopy image using a hybrid approach combining Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). The diagram details the steps from input image processing to classification, including feature extraction, graph construction with K-NN, application of various GNN models, node-level embedding, global pooling, and linear classification with softmax. " width="250" height="auto" />
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Architecture of the <t>CNN-GNN</t> pipeline for colon disease classification. The presentation of a detailed, step-by-step breakdown of the CNN-GNN pipeline, with each stage visually represented, highlighting the transition from raw medical images to classification outputs. <xref ref-type=Figure 5 illustrates the stages involved in analyzing a colonoscopy image using a hybrid approach combining Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). The diagram details the steps from input image processing to classification, including feature extraction, graph construction with K-NN, application of various GNN models, node-level embedding, global pooling, and linear classification with softmax. " width="250" height="auto" />
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Architecture of the <t>CNN-GNN</t> pipeline for colon disease classification. The presentation of a detailed, step-by-step breakdown of the CNN-GNN pipeline, with each stage visually represented, highlighting the transition from raw medical images to classification outputs. <xref ref-type=Figure 5 illustrates the stages involved in analyzing a colonoscopy image using a hybrid approach combining Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). The diagram details the steps from input image processing to classification, including feature extraction, graph construction with K-NN, application of various GNN models, node-level embedding, global pooling, and linear classification with softmax. " width="250" height="auto" />
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Architecture of the CNN-GNN pipeline for colon disease classification. The presentation of a detailed, step-by-step breakdown of the CNN-GNN pipeline, with each stage visually represented, highlighting the transition from raw medical images to classification outputs. <xref ref-type=Figure 5 illustrates the stages involved in analyzing a colonoscopy image using a hybrid approach combining Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). The diagram details the steps from input image processing to classification, including feature extraction, graph construction with K-NN, application of various GNN models, node-level embedding, global pooling, and linear classification with softmax. " width="100%" height="100%">

Journal: Frontiers in Physiology

Article Title: Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers

doi: 10.3389/fphys.2026.1734299

Figure Lengend Snippet: Architecture of the CNN-GNN pipeline for colon disease classification. The presentation of a detailed, step-by-step breakdown of the CNN-GNN pipeline, with each stage visually represented, highlighting the transition from raw medical images to classification outputs. Figure 5 illustrates the stages involved in analyzing a colonoscopy image using a hybrid approach combining Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). The diagram details the steps from input image processing to classification, including feature extraction, graph construction with K-NN, application of various GNN models, node-level embedding, global pooling, and linear classification with softmax.

Article Snippet: Alanazi et al ( ). showed that a simple CNN model, trained on the Kaggle H2E dataset, achieved 87% accuracy, outpacing several traditional classifiers.

Techniques: Extraction