{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-class-abnormality-classification-in","title":"Multi-Class Abnormality Classification in Video Capsule Endoscopy Using Deep Learning","arxiv_id":"2410.18879","date":"2024-10-24","proceeding":null,"authors":["Arnav Samal","Ranya Batsyas"],"abstract":"This report outlines Team Seq2Cure's deep learning approach for the Capsule Vision 2024 Challenge, leveraging an ensemble of convolutional neural networks (CNNs) and transformer-based architectures for multi-class abnormality classification in video capsule endoscopy frames. The dataset comprised over 50,000 frames from three public sources and one private dataset, labeled across 10 abnormality classes. To overcome the limitations of traditional CNNs in capturing global context, we integrated CNN and transformer models within a multi-model ensemble. Our approach achieved a balanced accuracy of 86.34 percent and a mean AUC-ROC score of 0.9908 on the validation set, earning our submission 5th place in the challenge. Code is available at http://github.com/arnavs04/capsule-vision-2024 .","url_abs":"https://arxiv.org/abs/2410.18879v2","url_pdf":"https://arxiv.org/pdf/2410.18879v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-class-abnormality-classification-in","repo_url":"https://github.com/arnavs04/capsule-vision-2024","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-class-classification-on-training-and","task":"Multi-class Classification","dataset":"Training and validation dataset of capsule vision 2024 challenge.","model":"Multi-Model Ensemble","rank_in_archive_order":1,"of":1,"metrics":{"Mean AUC":"0.9908"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}