# CapsoNet: A CNN-Transformer Ensemble for Multi-Class Abnormality Detection in Video Capsule Endoscopy

arXiv: https://arxiv.org/abs/2410.18879 · HTML: https://mlsanity.com/paper/2410.18879/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [What is the Effect of Importance Weighting in Deep Learning?](https://mlsanity.com/paper/1812.03372/) (2018)
- [Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy](https://mlsanity.com/paper/2408.04940/) (2024)

## Built on by (most cited first, 1 of 1)

- [Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification](https://mlsanity.com/paper/2411.01652/) (2024)

## Abstract (arXiv)

We present CapsoNet, a deep learning framework developed for the Capsule Vision 2024 Challenge, designed to perform multi-class abnormality classification in video capsule endoscopy (VCE) frames. CapsoNet leverages an ensemble of convolutional neural networks (CNNs) and transformer-based architectures to capture both local and global visual features. The model was trained and evaluated on a dataset of over 50,000 annotated frames spanning ten abnormality classes, sourced from three public and one private dataset. To address the challenge of class imbalance, we employed focal loss, weighted random sampling, and extensive data augmentation strategies. All models were fully fine-tuned to maximize performance within the ensemble. CapsoNet achieved a balanced accuracy of 86.34 percent and a mean AUC-ROC of 0.9908 on the official validation set, securing Team Seq2Cure 5th place in the competition. Our implementation is available at http://github.com/arnavs04/capsule-vision-2024