Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy

• URL: http://arxiv.org/abs/2411.01479v1
• Date: Sun, 03 Nov 2024 08:34:04 GMT
• Title: Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy
• Authors: Aakarsh Bansal, Bhuvanesh Singla, Raajan Rajesh Wankhade, Nagamma Patil,
• Abstract summary: We present an approach to developing a model for classifying abnormalities in video capsule endoscopy (VCE) frames. We implement a tiered augmentation strategy using the albumentations library to enhance minority class representation. Our pipeline, developed in PyTorch, employs a flexible architecture enabling seamless adjustments to classification complexity.
• Score: 1.124958340749622
• License:
• Abstract: This study presents an approach to developing a model for classifying abnormalities in video capsule endoscopy (VCE) frames. Given the challenges of data imbalance, we implemented a tiered augmentation strategy using the albumentations library to enhance minority class representation. Additionally, we addressed learning complexities by progressively structuring training tasks, allowing the model to differentiate between normal and abnormal cases and then gradually adding more specific classes based on data availability. Our pipeline, developed in PyTorch, employs a flexible architecture enabling seamless adjustments to classification complexity. We tested our approach using ResNet50 and a custom ViT-CNN hybrid model, with training conducted on the Kaggle platform. This work demonstrates a scalable approach to abnormality classification in VCE.

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