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
Accurate classification of fetal biometric planes in ultrasound imaging is more important for effective prenatal screening and early diagnosis of fetal abnormalities. To enhance the diagnostic efficiency, the research proposed a novel method called "Dynamic Graph-Based Quantum Feature Selection" (DG-QFS) framework to improve the classification performance by integrating the quantum computing principles. Features are extracted from ultrasound images using a pre-trained deep learning model and processed through a quantum-driven feature selection pipeline that models the inter-feature relationships using dynamically entangled multi-qubit graphs. In the DG-QFS method, qubits represent extracted deep feature nodes, while a quantum entanglement score-based dynamic graph captures the complex dependencies. Entanglement score and dynamic graph centrality are used to select the most informative features. The refined feature set is classified using a lightweight multi-layer perceptron (MLP), stochastic gradient descent with adaptive learning rate. Examined the proposed model on a fetal plane ultrasound scan dataset, including 12,400 images of six categorical planes, such as brain, thorax, abdomen, femur, maternal cervix, and other views. Experimental results demonstrate that the proposed model achieves a classification accuracy of 96.73%, significantly outperforming baseline deep learning and conventional feature selection techniques regarding accuracy, generalization, and interpretability.
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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