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Gastrointestinal (GI) diseases are among the most commonly occurring diseases in the human digestive system, with a significantly higher mortality rate. Early diagnosis plays a crucial role in the effective treatment of these diseases. Accurate evaluation of endoscopic images is essential in the decision making process for patient treatment. While many deep learning models, such as Convolutional Neural Networks (CNNs), have been employed for the detection of these diseases, they struggle to handle large datasets effectively. To overcome these limitations, we propose the use of advanced Vision Transformer (ViT) model, which shown superior performance on large-scale data. The proposed model is evaluated on GastroVision dataset which consists of 8600 images, categorized into 27 different classes. Our proposed approach achieved an recall of 85.75%, precision of 86.41%, and an F1-score of 85.57%, and also outperformed the Densenet-121 model.

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Multi-class Classification of Gastrointestinal Disease Detection Using Vision Transformers

  • 会议论文

  • 发表日期：

    2026年

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  • Jagadeesh Kakarla,
  • R. Usha Rani,
  • Vemakoti Krishnamurty,
  • Ruvva Pujitha

摘要

Gastrointestinal (GI) diseases are among the most commonly occurring diseases in the human digestive system, with a significantly higher mortality rate. Early diagnosis plays a crucial role in the effective treatment of these diseases. Accurate evaluation of endoscopic images is essential in the decision making process for patient treatment. While many deep learning models, such as Convolutional Neural Networks (CNNs), have been employed for the detection of these diseases, they struggle to handle large datasets effectively. To overcome these limitations, we propose the use of advanced Vision Transformer (ViT) model, which shown superior performance on large-scale data. The proposed model is evaluated on GastroVision dataset which consists of 8600 images, categorized into 27 different classes. Our proposed approach achieved an recall of 85.75%, precision of 86.41%, and an F1-score of 85.57%, and also outperformed the Densenet-121 model.