인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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
Skin cancer is the abnormal growth of skin cells, most often developing on skin exposed to the sun. It is among the most fatal forms of cancer, making its early detection and therapy crucial. In addition to conventional techniques, deep learning methods are increasingly utilized for accurate identification and classification. This study proposes a convolutional neural network (CNN) model using transfer learning to detect and classify multiple types of skin cancer. This study focuses on evaluating the efficacy of transfer learning techniques in enhancing CNN performance for this critical task. A significant contribution of this study is the use of transfer learning to improve CNN performance in skin cancer detection and classification by leveraging pre-trained models, including ResNet50, Xception, MobileNet, EfficientNetB0, and DenseNet121. The integration of metadata demonstrated a significant improvement in accuracy compared to using images alone, enhancing the performance of most models. Further enhancement was achieved through ensemble techniques, specifically an adaptive weighted ensemble method, which dynamically assigns weights to individual models based on their performance, resulting in superior overall accuracy. SMOTE was used as an oversampling technique to address class imbalance. The proposed fusion of pre-trained models (ResNet50, Xception, and EfficientNetB0) combined with metadata achieved 93.2% accuracy, 93% precision, 93% recall, 93% F1 score, and 97.3% AUC on the ISIC 2018 dataset. On the ISIC 2019 dataset, it achieved 91.1% accuracy, 92% precision, 93% recall, 92% F1 score, and 95.5% AUC, surpassing many state-of-the-art methods. Experiments on an external dataset, Derm7pt, resulted in 82.5% accuracy, with a precision of 86%, recall of 83%, F1 score of 84% and AUC of 89.15%, demonstrating the improved interpretability and generalization of the proposed model. The proposed ensemble model optimizes deep learning for healthcare applications, enhancing dermatological diagnosis and treatment strategies for skin cancer patients.
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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