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, especially melanoma, the most severe type, has increased in recent decades. It develops from cells that grow abnormally and can invade the surrounding tissue and spread throughout the body. Early and accurate diagnosis is essential to prevent disease progression and allow for less invasive clinical treatment. The extraction of complex dermoscopic images and the improvement of lesion classification performance have significantly improved skin cancer diagnosis through the use of convolutional neural networks (CNNs). In this study, a novel deep convolutional neural network that combines ConvNeXt and Vision Transformer (ViT) architectures through an adaptive attention-based approach for advanced feature fusion to automatically multi-classify skin cancer samples. This model is evaluated on two dermoscopy benchmark datasets, including ISIC-2019 and HAM10000 and both datasets reflect the real-world problem of class imbalance. The evaluation results of MedFusionNet are calculated using various evaluation metrics, including accuracy, precision, recall and AUC and compared with deep learning algorithms such as ResNet50, MobileNet V2, DenseNet121 and ViT-B16. The experimental results show that MedFusionNet outperforms the current models with a classification accuracy of 98.80% and 97.90% for HAM10000 and ISIC-2019, respectively. Grad-CAM visualizations qualitatively show that the model focuses on clinically relevant lesion regions, providing interpretive insight without claiming complete causal explainability. The results show that the proposed model can efficiently handle multi-class tasks in dermatological imaging and MedFusionNet is a suitable choice for implementation in real-world computer-aided diagnosis systems.
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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