Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification
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Abstract (arXiv)
In recent years, the diagnosis of gastrointestinal (GI) diseases has advanced greatly with the advent of high-tech video capsule endoscopy (VCE) technology, which allows for non-invasive observation of the digestive system. The MisaHub Capsule Vision Challenge encourages the development of vendor-independent artificial intelligence models that can autonomously classify GI anomalies from VCE images. This paper presents CNN architecture designed specifically for multiclass classification of ten gut pathologies, including angioectasia, bleeding, erosion, erythema, foreign bodies, lymphangiectasia, polyps, ulcers, and worms as well as their normal state.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 3 Nov 2024.
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9 of 11 references.
References · 9
- 2024Frontiers in Intelligent Colonoscopycited by 14
- 2024Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformationscited by 3
- 2024Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal Endoscopycited by 3
- 2024CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopycited by 2
- 2024CASCRNet: An Atrous Spatial Pyramid Pooling and Shared Channel Residual based Network for Capsule Endoscopycited by 1
- 2024Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Imagescited by 1
- 2024CapsoNet: A CNN-Transformer Ensemble for Multi-Class Abnormality Detection in Video Capsule Endoscopycited by 1
- 2024CapsuleNet: A Deep Learning Model To Classify GI Diseases Using EfficientNet-b7cited by 1
- 2024A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERTcited by 1
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