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Open AccessArticle

Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

1
eVida Research Group, University of Deusto, 48007 Bilbao, Spain
2
Biokeralty Reseach Institute, 01510 Vitoria, Spain
3
Department of Pathological Anatomy, University Hospital of Araba, 01009 Vitoria, Spain
4
Clinica Colsanitas, Bogotá 110221, Colombia
*
Author to whom correspondence should be addressed.
These authors share first authorship.
Sensors 2020, 20(16), 4373; https://doi.org/10.3390/s20164373
Received: 10 June 2020 / Revised: 1 August 2020 / Accepted: 3 August 2020 / Published: 5 August 2020
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging and Sensing)
Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images. View Full-Text
Keywords: deep learning; histopathology; breast cancer; image classification; ensemble models deep learning; histopathology; breast cancer; image classification; ensemble models
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MDPI and ACS Style

Hameed, Z.; Zahia, S.; Garcia-Zapirain, B.; Javier Aguirre, J.; María Vanegas, A. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors 2020, 20, 4373. https://doi.org/10.3390/s20164373

AMA Style

Hameed Z, Zahia S, Garcia-Zapirain B, Javier Aguirre J, María Vanegas A. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors. 2020; 20(16):4373. https://doi.org/10.3390/s20164373

Chicago/Turabian Style

Hameed, Zabit; Zahia, Sofia; Garcia-Zapirain, Begonya; Javier Aguirre, José; María Vanegas, Ana. 2020. "Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models" Sensors 20, no. 16: 4373. https://doi.org/10.3390/s20164373

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