Breast cancer histopathology image classification using an ensemble of deep learning models

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dc.contributor.authorHameed, Zabit
dc.contributor.authorZahia, Sofia
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorAguirre, José Javier
dc.contributor.authorVanegas, Ana María
dc.date.accessioned2026-03-13T11:52:20Z
dc.date.available2026-03-13T11:52:20Z
dc.date.issued2020-08-02
dc.date.updated2026-03-13T11:52:20Z
dc.description.abstractBreast 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.en
dc.description.sponsorshipAcknowledgment to the Basque Country project MIFLUDAN that partially provided funds for this work in collaboration with eVida Research Group IT 905-16, University of Deusto, Bilbao, Spainen
dc.identifier.citationHameed, Z., Zahia, S., Garcia-Zapirain, B., Aguirre, J. J., & Vanegas, A. M. (2020). Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors (Switzerland), 20(16), 1-17. https://doi.org/10.3390/S20164373
dc.identifier.doi10.3390/S20164373
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5441
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland
dc.subject.otherDeep learning
dc.subject.otherHistopathology
dc.subject.otherBreast cancer
dc.subject.otherImage classification
dc.subject.otherEnsemble models
dc.titleBreast cancer histopathology image classification using an ensemble of deep learning modelsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage17
oaire.citation.issue16
oaire.citation.startPage1
oaire.citation.titleSensors (Switzerland)
oaire.citation.volume20
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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