{"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/effective-deep-learning-for-semantic","title":"Effective Deep Learning for Semantic Segmentation Based Bleeding Zone Detection in Capsule Endoscopy Images","arxiv_id":null,"date":"2018-09-06","proceeding":null,"authors":["Tonmoy Ghosh","Linfeng Li","Jacob Chakareski"],"abstract":"Capsule endoscopy (CE) is a non-invasive way to detect small intestinal abnormalities such as bleeding. It provides a direct vision of the patients entire gastrointestinal (GI) tract. However, a manual inspection of the huge number of images produced thereby is tedious and lengthy, and thus prone to human errors. This makes automated computer assisted decision- making appealing in this context. This paper introduces a novel deep-learning based semantic segmentation approach for bleeding zone detection in CE images. A bleeding image features three regions labeled as bleeding, non-bleeding, and background. Thus, a convolutional neural network (CNN) is trained using SegNet layers with three classes. A given CE image is segmented using our training network and the detected bleeding zones are marked. The proposed network architecture is tested on different color planes and best performance is achieved using the hue saturation and value (HSV) color space. Experimental performance evaluation is carried out on a publicly available clinical dataset, on which our framework achieves 94.42% global accuracy and 90.69% weighted intersection over union (IoU), two state-of-the-art classification metrics. Performance gains are demonstrated over several recent state-of-art competiting meth- ods in terms of all performance measures we examined, including mean accuracy, mean IoU, global accuracy and weighted IoU.\r\nIndex Terms—bleeding detection, capsule endoscopy, deep learning, SegNet, convolutional neural network.","url_abs":"https://ieeexplore.ieee.org/abstract/document/8451300","url_pdf":"https://www.researchgate.net/profile/Tonmoy-Ghosh/publication/327995579_Effective_Deep_Learning_for_Semantic_Segmentation_Based_Bleeding_Zone_Detection_in_Capsule_Endoscopy_Images/links/5d6e843445851542789f2f72/Effective-Deep-Learning-for-Semantic-Segmentation-Based-Bleeding-Zone-Detection-in-Capsule-Endoscopy-Images.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":"effective-deep-learning-for-semantic","repo_url":"https://github.com/Tonmoy-Ghosh/Semantic-Segmentation-Based-Bleeding-Zone-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"segnet","method_name":"SegNet"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}