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
The aim of computer vision techniquesand deep learning in the era of digitalization is to derive valuable insights from them and generate novel understanding. This makes it possible to employ imaging to quickly diagnose and treat a variety of diseases. In the field of dermatology, deep neural networks are utilized to differentiate between images of melanoma and non-melanoma skin lesions. In this paper, we have emphasised two important aspects of melanoma detection research. The accuracy of classifiers is the first thing to take into account, even with very little modifications to the dataset's characteristics there will be a lot of difference in accuracy. We investigated transfer learning issues in this case. We propose that continual training-test iterations are necessary to create reliable prediction models based on the results of the initial study.The second argument is the need for a system with a flexible design that can accommodate changes to training datasets.Our proposal for creating and implementing a melanoma detection service that utilizes clinical and thermoscopic images involves the development and implementing a hybrid architecture that fuses fog, edge and cloud computing. In addition, this design should aim to decrease the duration of the ongoing retraining process, which is necessary to accommodate the large volume of data that requires evaluation. This notion has been reinforced by experiments using a single computer and a variety of distribution techniques, which show how a dispersed strategy ensures output attainment in a noticeably more sufficient amount of time.
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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