International Journal of Computational Intelligence Systems

Volume 14, Issue 1, 2021, Pages 1003 - 1013

Deep Learning Models Combining for Breast Cancer Histopathology Image Classification

Authors
Hela Elmannai*, ORCID, Monia HamdiORCID, Abeer AlGarni
Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11564, Saudi Arabia
*Corresponding author. Email: hela.elmannai@gmail.com; hselmannai@pnu.edu.sa
Corresponding Author
Hela Elmannai
Received 13 November 2020, Accepted 19 February 2021, Available Online 8 March 2021.
DOI
10.2991/ijcis.d.210301.002How to use a DOI?
Keywords
Breast cancer; Histopathology images; Deep learning; Tssue malignancy; Classification
Abstract

Breast cancer is one of the foremost reasons of death among women in the world. It has the largest mortality rate compared to the types of cancer accounting for 1.9 million per year in 2020. An early diagnosis may increase the survival rates. To this end, automating the analysis and the diagnosis allows to improve the accuracy and to reduce processing time. However, analyzing breast imagery's is non-trivial and may lead to experts' disagreements. In this research, we focus on breast cancer histopathological images acquired using the microscopic scan of breast tissues. We present combined two deep convolutional neural networks (DCNNs) to extract distinguished image features using transfer learning. The pre-trained Inception and the Xceptions models are used in parallel. Then, the feature maps are combined and reduced by dropout before being fed to the last fully connected layers for classification. We follow a sub-image classification then a whole image classification based on majority vote and maximum probability rules. Four tissue malignancy levels are considered: normal, benign, in situ carcinoma, and invasive carcinoma. The experimentations are performed to the Breast Cancer Histology (BACH) dataset. The overall accuracy for the sub-image classification is 97.29% and for the carcinoma cases the sensitivity achieved 99.58%. The whole image classification overall accuracy reaches 100% by majority vote and 95% by maximum probability fusion decision. The numerical results showed that our proposed approach outperforms the previous methods in terms of accuracy and sensitivity. The proposed design allows an extension to whole-slide histology images classification.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
1003 - 1013
Publication Date
2021/03/08
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.210301.002How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Hela Elmannai
AU  - Monia Hamdi
AU  - Abeer AlGarni
PY  - 2021
DA  - 2021/03/08
TI  - Deep Learning Models Combining for Breast Cancer Histopathology Image Classification
JO  - International Journal of Computational Intelligence Systems
SP  - 1003
EP  - 1013
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210301.002
DO  - 10.2991/ijcis.d.210301.002
ID  - Elmannai2021
ER  -