Multi-Class Abnormality Classification Task in Video Capsule Endoscopy
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929612696584192 |
|---|---|
| author | Verma, Dev Rishi Saxena, Vibhor Sharma, Dhruv Gupta, Arpan |
| author_facet | Verma, Dev Rishi Saxena, Vibhor Sharma, Dhruv Gupta, Arpan |
| contents | In this work for Capsule Vision Challenge 2024, we addressed the challenge of multiclass anomaly classification in video capsule Endoscopy (VCE)[1] with a variety of deep learning models, ranging from custom CNNs to advanced transformer architectures. The purpose is to correctly classify diverse gastrointestinal disorders, which is critical for increasing diagnostic efficiency in clinical settings. We started with a baseline CNN model and improved performance with ResNet[2] for better feature extraction, followed by Vision Transformer (ViT)[3] to capture global dependencies. We further improve the results by using Multiscale Vision Transformer (MViT)[4] for improved hierarchical feature extraction, while Dual Attention Vision Transformer (DaViT) [5] delivered best results by combining spatial and channel attention methods. Our best balanced accuracy on validation set [6] was 0.8592 and Mean AUC was 0.9932. This methodology enabled us to improve model accuracy across a wide range of criteria, greatly surpassing all other methods.Additionally, our team capsule commandos achieved 7th place ranking with a test set[7] performance of Mean AUC: 0.7314 and balanced accuracy: 0.3235 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19973 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Class Abnormality Classification Task in Video Capsule Endoscopy Verma, Dev Rishi Saxena, Vibhor Sharma, Dhruv Gupta, Arpan Image and Video Processing Computer Vision and Pattern Recognition In this work for Capsule Vision Challenge 2024, we addressed the challenge of multiclass anomaly classification in video capsule Endoscopy (VCE)[1] with a variety of deep learning models, ranging from custom CNNs to advanced transformer architectures. The purpose is to correctly classify diverse gastrointestinal disorders, which is critical for increasing diagnostic efficiency in clinical settings. We started with a baseline CNN model and improved performance with ResNet[2] for better feature extraction, followed by Vision Transformer (ViT)[3] to capture global dependencies. We further improve the results by using Multiscale Vision Transformer (MViT)[4] for improved hierarchical feature extraction, while Dual Attention Vision Transformer (DaViT) [5] delivered best results by combining spatial and channel attention methods. Our best balanced accuracy on validation set [6] was 0.8592 and Mean AUC was 0.9932. This methodology enabled us to improve model accuracy across a wide range of criteria, greatly surpassing all other methods.Additionally, our team capsule commandos achieved 7th place ranking with a test set[7] performance of Mean AUC: 0.7314 and balanced accuracy: 0.3235 |
| title | Multi-Class Abnormality Classification Task in Video Capsule Endoscopy |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.19973 |