Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network

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dc.contributor.authorHameed, Zabit
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorAguirre, José Javier
dc.contributor.authorIsaza-Ruget, Mario Arturo
dc.date.accessioned2024-11-14T11:37:25Z
dc.date.available2024-11-14T11:37:25Z
dc.date.issued2022-12
dc.date.updated2024-11-14T11:37:25Z
dc.description.abstractBreast cancer is a common malignancy and a leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity and mortality rates in women. To this end, histopathological diagnosis is usually followed as the gold standard approach. However, this process is tedious, labor-intensive, and may be subject to inter-reader variability. Accordingly, an automatic diagnostic system can assist to improve the quality of diagnosis. This paper presents a deep learning approach to automatically classify hematoxylin-eosin-stained breast cancer microscopy images into normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma using our collected dataset. Our proposed model exploited six intermediate layers of the Xception (Extreme Inception) network to retrieve robust and abstract features from input images. First, we optimized the proposed model on the original (unnormalized) dataset using 5-fold cross-validation. Then, we investigated its performance on four normalized datasets resulting from Reinhard, Ruifrok, Macenko, and Vahadane stain normalization. For original images, our proposed framework yielded an accuracy of 98% along with a kappa score of 0.969. Also, it achieved an average AUC-ROC score of 0.998 as well as a mean AUC-PR value of 0.995. Specifically, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. For normalized images, the proposed architecture performed better for Makenko normalization compared to the other three techniques. In this case, the proposed model achieved an accuracy of 97.79% together with a kappa score of 0.965. Also, it attained an average AUC-ROC score of 0.997 and a mean AUC-PR value of 0.991. Especially, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. These results demonstrate that our proposed model outperformed the baseline AlexNet as well as state-of-the-art VGG16, VGG19, Inception-v3, and Xception models with their default settings. Furthermore, it can be inferred that although stain normalization techniques offered competitive performance, they could not surpass the results of the original dataset.en
dc.description.sponsorshipAcknowledgment to the Basque Country project MIFLUDAN (Elkartek call) that partially provided funds for this work in collaboration with eVida Research Group IT 905-16 and FPI Grant, University of Deusto, Bilbao, Spainen
dc.identifier.citationHameed, Z., Garcia-Zapirain, B., Aguirre, J. J., & Isaza-Ruget, M. A. (2022). Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network. Scientific Reports, 12(1). https://doi.org/10.1038/S41598-022-19278-2
dc.identifier.doi10.1038/S41598-022-19278-2
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1857
dc.language.isoeng
dc.publisherNature Research
dc.rights© The Author(s) 2022
dc.titleMulticlass classification of breast cancer histopathology images using multilevel features of deep convolutional neural networken
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleScientific Reports
oaire.citation.volume12
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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