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
Lens-less digital in-line holographic microscopy (DIHM) is a low-cost, wide-field imaging technique that relies on computational reconstruction to form focused images that should ideally be free of twin-image artifacts. While current DIHM-based pollen classification systems are typically automated and rely on large datasets and deep learning, our study explored whether iteratively reconstructed DIHM images using the Gerchberg–Saxton (GS) algorithm are suitable for visual classification by human experts. Two veterinary cytopathologists evaluated images of six clinically relevant pollen types, namely timothy grass, common ragweed, silver birch, common alder, olive tree, and hazel, using both lens-less DIHM and conventional optical microscopy. Classification accuracy was comparable across modalities, with DIHM achieving 95.8% and optical microscopy 96.9%. Inter-observer agreement was high (Cohen’s κ = 0.91), indicating near-perfect consistency between evaluators. Most misclassifications involved silver birch pollen, likely due to its morphological variability and overlap with common alder and hazel. These findings demonstrate that lens-less DIHM combined with iterative reconstruction enables accurate visual identification of allergenic pollen, offering a promising alternative to conventional microscopy in veterinary and other resource-limited settings.
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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