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Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này:
https://elib.vku.udn.vn/handle/123456789/2740
Nhan đề: | An Improved Transfer Learning-Based Approach for Detecting Abnormal Cervical Cells from Pap Smear Microscopic Images |
Tác giả: | Le, Thi Thu Nga Pham, Vu Thu Nguyet |
Từ khoá: | Deep Learning transfer learning screening abnormalities detection cervical cells |
Năm xuất bản: | thá-2023 |
Nhà xuất bản: | Springer Nature |
Tóm tắt: | Cervical cancer is one of the most common and dangerous diseases for women's health. Automated abnormal cell screening will help detect early and improve the accuracy of cervical cancer diagnosis, from which women can plan treatment, preventing the development of cervical cancer and improving survival rates. This paper proposes an improved method to detect abnormal cervical cells from images of cells that are stained and examined under a microscope based on a transfer learning approach. Achieved results show that the proposed EfficientNetv2L-SVM model has an accuracy of 92% and an F1-score of 88%, higher than the ResNet50v2 deep learning model with 90% and 83%, respectively. The measurement of Precision and Recall also gives the same result. Experimental data were collected from the Cytopathology Laboratory of Binh Dinh province over ten years, from 2014 to 2023 with five different types of cervical cells based on the diagnosis of expert pathologists (The dataset in this research is provided and allowed to use by the Cytopathology Laboratory, located at Quy Nhon City, Binh Dinh Province, VietNam.). |
Mô tả: | Lecture Notes in Networks and Systems (LNNS, volume 734); CITA: Conference on Information Technology and its Applications; pp: 126-137. |
Định danh: | https://link.springer.com/chapter/10.1007/978-3-031-36886-8_11 http://elib.vku.udn.vn/handle/123456789/2740 |
ISBN: | 978-3-031-36886-8 |
Bộ sưu tập: | CITA 2023 (International) |
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