ArticleHealth information science and systems2024
Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset.
Article in Health information science and systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.Scientific reports · 2026Article
- An Attention-Enhanced ViT-HLNN Hybrid Ensemble Framework for Multi-Class Gastrointestinal Disease Classification.Scientific reports · 2026Article
- An EfficientNet-based hierarchical dual-encoder framework for multi-scale gastrointestinal disease detection.Scientific reports · 2026Article
- LightGastroFormer: a lightweight multi-resolution transformer for gastrointestinal disease classification.Scientific reports · 2026Article
- Grad-CAM Enhanced Explainable Deep Learning for Multi-Class Lung Cancer Classification Using DE-SAMNet Model.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based classification of colonoscopic images using an attention-enhanced ConvNeXt V2 architecture.Frontiers in oncology · 2026Article
- Automatic detection of gastrointestinal system abnormalities using deep learning-based segmentation and classification methods.Health information science and systems · 2025Article
- Hybrid deep learning with attention fusion for enhanced colon cancer detection.Scientific reports · 2025Article
- Deep Q-Learning for Gastrointestinal Disease Detection and Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Biomimetic Transfer Learning-Based Complex Gastrointestinal Polyp Classification.Biomimetics (Basel, Switzerland) · 2025Article
- A recurrent multimodal sparse transformer framework for gastrointestinal disease classification.Scientific reports · 2025Article
- Software-Based Transformation of White Light Endoscopy Images to Hyperspectral Images for Improved Gastrointestinal Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images.BMC medical informatics and decision making · 2025Article
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- Coronary artery disease classification using ConvMixer based classifier from CT angiography images.PeerJ. Computer science · 2025Article
- Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques.BMC medical imaging · 2024Article
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3 authors.
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Abstract
Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn's disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture's results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.
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