인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Detection of gastrointestinal bleeding in Wireless Capsule Endoscopy (WCE) images and accurate bleeding region segmentation and classification is crucial for exact diagnosis and treatment, as early detection can prevent severe complications. However, it remains challenging due to the inability of current methods to effectively differentiate between types of bleeding and handle complex borders of lesions. In this paper, a new framework: Multi-Stage Convo-Enhanced Retinex Canny DeepLabV3+ FusionNet is proposed to better tackle these challenges. Existing feature extraction algorithms struggle with colour differentiation and texture recognition, often failing to miss fine-scale textures that distinguish active bleeding from coagulated blood effectively. Hence, this approach is initiated with Clip-BiRetinexNet for preprocessing, enhancing image contrast and color consistency using Clipped Histogram Equalization and Bilateral Filtered Retinex thereby capturing fine-scale textures that distinguish active bleeding from coagulated blood. Existing segmentation and classification methods struggle with irregular and complex borders of bleeding types like ulcers and vascular lesions due to ineffective border detection. Therefore, the segmentation in this proposed model is handled by Hough Canny-Frangi Enhanced DeepLabV3+, improving edge detection and vascular pattern enhancement to delineate accurately irregular lesions. Next, a ResNet-NaiveBayes Fusion was shown for classification, offering effective probabilistic classification. The implementation results show that the proposed approach outperforms the state-of-the-art methods with a high mean pixel accuracy of 97.6%, classification accuracy of 99.2% and Dice Similarity Coefficient of 99.6%.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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