RACNet: risk assessment Net of cervical lesions in colposcopic images-Reference-Cited by-同舟云学术

RACNet: risk assessment Net of cervical lesions in colposcopic images

Published:2022-08-08 Issue:1 Volume:34 Page:2139-2157
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Xu Tianxiang¹^ORCID,Liu Peizhong¹^ORCID,Li Ping²,Wang Xiaoxia³^ORCID,Xue Huifeng⁴,Guo JingMing⁵,Dong Binhua⁶,Sun Pengming⁶^ORCID

Affiliation:

1. College of Engineering, Huaqiao University, Quanzhou, People’s Republic of China

2. Department of Gynecology and Obstetrics, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, People’s Republic of China

3. School of Medicine, Huaqiao University, Quanzhou, People’s Republic of China

4. Department of Fujian Provincial Cervical Disease Diagnosis and Treatment Health Center, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, People’s Republic of China

5. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

6. Laboratory of Gynecologic Oncology, Fujian Provincial Maternity and Children’s Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, People’s Republic of China

Funder

National Natural Science Foundation of Fujian

Quanzhou Scientific and Technological Planning Projects

Fujian Provincial Science and Technology Major Project

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2022.2085665

Reference43 articles.

1. Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope

2. Cervical Lesion Detection Net

3. Detection of cervical lesion region from colposcopic images based on feature reselection

4. Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

5. Bochkovskiy, A., Wang, C.Y. & Liao, H.Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. ArXiv, abs/2004.10934.

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