TY - JOUR
T1 - An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification
AU - Xue, Dan
AU - Zhou, Xiaomin
AU - Li, Chen
AU - Yao, Yudong
AU - Rahaman, Md Mamunur
AU - Zhang, Jinghua
AU - Chen, Hao
AU - Zhang, Jinpeng
AU - Qi, Shouliang
AU - Sun, Hongzan
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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%.
AB - 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%.
KW - Cervical cancer
KW - classification
KW - differentiation stages
KW - ensemble learning
KW - histopathology images
KW - transfer learning
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U2 - 10.1109/ACCESS.2020.2999816
DO - 10.1109/ACCESS.2020.2999816
M3 - Article
AN - SCOPUS:85086744005
VL - 8
SP - 104603
EP - 104618
JO - IEEE Access
JF - IEEE Access
M1 - 9107128
ER -