Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial‐aware booster. Issue 17 (25th February 2021)

• Record Type: Journal Article
• Authors: Ma, Dongyang; Liu, Jinhua; Li, Jing; Zhou, Yuanfeng
• Is Part Of: IET image processing. Volume 14:Issue 17(2020)
• Page Start: 4717
• Page End: 4725
• Publication Date: 2021-02-25
• Subjects: medical image processing -- gynaecology -- cellular biophysics -- object detection -- biomedical optical imaging -- image segmentation -- cancer; intelligent diagnosis technologies -- cervical cancer screening -- cervical cancer detection algorithms -- object detection technology -- cervical smear images -- booster -- cervical cancer cells -- image processing technology -- cervical lesion detection; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0688
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid‐based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial‐aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively.