An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification

Dan Xue, Xiaomin Zhou, Chen Li, Yudong Yao, Md Mamunur Rahaman, Jinghua Zhang, Hao Chen, Jinpeng Zhang, Shouliang Qi, Hongzan Sun

Research output: Contribution to journalArticlepeer-review

119 Scopus citations

Abstract

In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in cervical histopathology image analysis. However, there have been very few investigations that have described the stages of differentiation of cervical histopathological images. Therefore, in this article, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderate and poorly differentiated cervical histopathological images. First of all, we have developed Inception-V3, Xception, VGG-16, and Resnet-50 based TL structures. Then, to enhance the classification performance, a weighted voting based EL strategy is introduced. After that, to evaluate the proposed algorithm, a dataset consisting of 307 images, stained by three immunohistochemistry methods (AQP, HIF, and VEGF) is considered. In the experiment, we obtain the highest overall accuracy of 97.03% and 98.61% on AQP staining images and poor differentiation of VEGF staining images, individually. Finally, an additional experiment for classifying the benign cells from the malignant ones is carried out on the Herlev dataset and obtains an overall accuracy of 98.37%.

Original languageEnglish
Article number9107128
Pages (from-to)104603-104618
Number of pages16
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Cervical cancer
  • classification
  • differentiation stages
  • ensemble learning
  • histopathology images
  • transfer learning

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