Chen, E. and Ting, Hua-Nong and Chuah, Joon Huang and Zhao, Jun (2024) Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey. IEEE Access, 12. pp. 114170-114189. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3445371.
Full text not available from this repository.Abstract
Pap smear testing is crucial for early diagnosis of cervical cancer, but cell overlapping poses a significant challenge to diagnostic accuracy, as improper processing of overlapping cells can lead to misclassification. While significant research efforts have been devoted to segmenting overlapping cells, there is an absence of thorough reviews covering existing studies. This survey represents the first comprehensive exploration of technologies aiming to segment overlapping cells in cervical cytology images. Initially, we collected over 100 relevant papers from various open-source databases using diverse keywords. Subsequently, we conducted a thorough analysis covering various aspects, including datasets, evaluation methods, and data augmentation techniques. We then categorized the applications into conventional machine learning and deep learning approaches, further subdividing both methods into three groups. We summarized articles that utilized conventional machine learning methods and compared the outcomes with those employing deep learning methods. Finally, we provide insights into current challenges and prospects in this critical domain.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Image segmentation; Measurement; Surveys; Cervical cancer; Testing; Indexes; Machine learning; Cervical cells; overlapping; segmentation; machine learning; survey |
| Subjects: | R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 03 Jan 2025 08:37 |
| Last Modified: | 03 Jan 2025 08:37 |
| URI: | http://eprints.um.edu.my/id/eprint/47077 |
Actions (login required)
 |
View Item |
