A survey for cervical cytopathology image analysis using deep learning

Md Mamunur Rahaman, Chen Li, Xiangchen Wu, Yudong Yao, Zhijie Hu, Tao Jiang, Xiaoyan Li, Shouliang Qi

Research output: Contribution to journalReview articlepeer-review

99 Scopus citations

Abstract

Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

Original languageEnglish
Article number9046839
Pages (from-to)61687-61710
Number of pages24
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Cervical cancer
  • classification
  • convolutional neural networks
  • cytopathology
  • deep learning
  • pap smear
  • segmentation

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