Multi-Class Abnormality Classification in Video Capsule Endoscopy Using Deep Learning

• URL: http://arxiv.org/abs/2410.18879v2
• Date: Sun, 01 Dec 2024 11:58:37 GMT
• Title: Multi-Class Abnormality Classification in Video Capsule Endoscopy Using Deep Learning
• Authors: Arnav Samal, Ranya Batsyas,
• Abstract summary: This report outlines Team Seq2Cure's deep learning approach for the Capsule Vision 2024 Challenge.<n>We leverage an ensemble of convolutional neural networks (CNNs) and transformer-based architectures for multi-class abnormality classification in video capsule endoscopy frames.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 .

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