Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems
| العنوان: | Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems |
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| المؤلفون: | Youngki Lee, Jungmo Ahn, Rajesh Krishna Balan, Huynh Nguyen Loc, JeongGil Ko |
| المصدر: | Computer. 51:68-76 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | General Computer Science, Image quality, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Convolutional neural network, law.invention, 03 medical and health sciences, 0302 clinical medicine, Capsule endoscopy, law, 0202 electrical engineering, electronic engineering, information engineering, medicine, Computer vision, Focus (computing), medicine.diagnostic_test, business.industry, Process (computing), Image capture, Small intestine, Endoscopy, medicine.anatomical_structure, 030211 gastroenterology & hepatology, 020201 artificial intelligence & image processing, Artificial intelligence, business, Image based |
| الوصف: | Capsule endoscopy identifies damaged areas in a patient’s small intestine but often outputs poor-quality images or misses lesions, leading to either misdiagnosis or repetition of the lengthy procedure. The authors propose applying deep-learning models to automatically process the captured images and identify lesions in real time, enabling the capsule to take additional images of a specific location, adjust its focus level, or improve image quality. The authors also describe the technical challenges in realizing a viable automated capsule-endoscopy system. |
| تدمد: | 1558-0814 0018-9162 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::fda4a874bc0c8e2ef4e7db50d009bb3c https://doi.org/10.1109/mc.2018.2381116 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........fda4a874bc0c8e2ef4e7db50d009bb3c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580814 00189162 |
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