Open Set Recognition for Endoscopic Image Classification: A Deep Learning Approach on the Kvasir Dataset
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908416757202944 |
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| author | Moazzami, Kasra Son, Seoyoun Lin, John Lee, Sun Min Son, Daniel Lee, Hayeon Lee, Jeongho Lee, Seongji |
| author_facet | Moazzami, Kasra Son, Seoyoun Lin, John Lee, Sun Min Son, Daniel Lee, Hayeon Lee, Jeongho Lee, Seongji |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open Set Recognition for Endoscopic Image Classification: A Deep Learning Approach on the Kvasir Dataset Moazzami, Kasra Son, Seoyoun Lin, John Lee, Sun Min Son, Daniel Lee, Hayeon Lee, Jeongho Lee, Seongji Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Open Set Recognition for Endoscopic Image Classification: A Deep Learning Approach on the Kvasir Dataset |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.18284 |