Open Set Recognition for Endoscopic Image Classification: A Deep Learning Approach on the Kvasir Dataset
This work addresses the challenge of handling unseen conditions in medical diagnostics for endoscopy, though it is incremental as it applies existing OSR methods to a new dataset.
The paper tackled the problem of open set recognition for endoscopic image classification on the Kvasir dataset, evaluating deep learning models like ResNet-50 and Swin Transformer with OpenMax, and found that OSR techniques are crucial for reliable AI deployment in clinical settings.
Endoscopic image classification plays a pivotal role in medical diagnostics by identifying anatomical landmarks and pathological findings. However, conventional closed-set classification frameworks are inherently limited in open-world clinical settings, where previously unseen conditions can arise andcompromise model reliability. To address this, we explore the application of Open Set Recognition (OSR) techniques on the Kvasir dataset, a publicly available and diverse endoscopic image collection. In this study, we evaluate and compare the OSR capabilities of several representative deep learning architectures, including ResNet-50, Swin Transformer, and a hybrid ResNet-Transformer model, under both closed-set and open-set conditions. OpenMax is adopted as a baseline OSR method to assess the ability of these models to distinguish known classes from previously unseen categories. This work represents one of the first efforts to apply open set recognition to the Kvasir dataset and provides a foundational benchmark for evaluating OSR performance in medical image analysis. Our results offer practical insights into model behavior in clinically realistic settings and highlight the importance of OSR techniques for the safe deployment of AI systems in endoscopy.