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.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
With the continuous growth of medical data, traditional learning methods have limitations in processing private data and model training. A federated learning differential privacy medical image classification algorithm based on hybrid reality technology was proposed to conduct medical image analysis in a distributed environment. Firstly, the differential privacy medical image classification based on federated learning was studied. Then, the results of the differential privacy medical image classification model based on federated learning were analyzed. The results confirmed that as the learning rate increased, the accuracy of the research algorithm gradually increased. When the learning rate was 0.1 and the iterations were 10, the research algorithm reached the highest accuracy, which was 99.82%. In addition, the study also analyzed the impact of the number of clients on algorithm performance. In the range of 8 to 12 clients, this research algorithm demonstrated significant advantages compared to traditional image classification algorithms. When the number of clients was 8, its AUC value reached its highest, at 92.23%. In summary, the research algorithm has shown significant advantages in medical image classification tasks.
본문·목차
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UCI(KEPA) : I410-151-26-02-094303423
