Unsupervised histopathological sub-image analysis for breast cancer diagnosis using variational autoencoders, clustering, and supervised learning

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dc.contributor.authorAbdulaal, Alaa Hussein
dc.contributor.authorValizadeh, Morteza
dc.contributor.authorYassin, Riyam Ali
dc.contributor.authorAlbaker, Baraa M.
dc.contributor.authorAbdulwahhab, Ali H.
dc.contributor.authorAmirani, Mehdi Chehel
dc.contributor.authorShah, A. F. M. Shahen
dc.date.accessioned2024-12-04T12:26:33Z
dc.date.available2024-12-04T12:26:33Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThis paper presents an integrated approach to breast cancer diagnosis that combines unsupervised and supervised learning techniques. The method involves using a pre-trained VGG19 model to process sub-images from the BreaKHis dataset, divided into nine parts for comprehensive analysis. This will be followed by a complete description of the architecture and workings of the variational Autoencoder (VAE) used for unsupervised Learning. The encoder network maps the input features to lower dimensions, capturing the most essential information. VAE learns a compressed representation of sub-images, facilitating a more profound understanding of underlying patterns and structures. For this reason, we then employ k-means clustering on the encoded representation to find naturally occurring clusters in our data set comprising a histopathological image. Every single sub-image is later fed into the VGG19-SVM model for classification purposes. During magnification at 100x, this model has attained a fantastic accuracy rate of 98.56%. Combining unsupervised analysis with VAE/k-means clustering and supervised classification with VGG19/SVM can integrate information from both methods, thereby improving the accuracy and robustness of such a task as sub-image classification in breast cancer histopathology.en_US
dc.identifier.citationAbdulaal, A. H., Valizadeh, M., Yassin, R. A., Albaker, B. M., Abdulwahhab, A. H., Amirani, M. C., Shah, A. F. M. S. (2024). Unsupervised histopathological sub-image analysis for breast cancer diagnosis using variational autoencoders, clustering, and supervised learning. Journal of Engineering and Sustainable Development, 28(6), 729-744. 10.31272/jeasd.28.6.6en_US
dc.identifier.endpage744en_US
dc.identifier.issn2520-0917
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85209086650
dc.identifier.scopusqualityQ4
dc.identifier.startpage729en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5067
dc.identifier.volume28en_US
dc.indekslendigikaynakScopus
dc.institutionauthorAbdulwahhab, Ali H.
dc.language.isoen
dc.publisherMustansiriyah University College of Engineeringen_US
dc.relation.ispartofJournal of Engineering and Sustainable Development
dc.relation.isversionof10.31272/jeasd.28.6.6en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast cancer diagnosisen_US
dc.subjectClusteringen_US
dc.subjectConvolutional neural networksen_US
dc.subjectFeature extractionen_US
dc.subjectSupervised Learningen_US
dc.subjectUnsupervised Learningen_US
dc.subjectVariational Autoencodersen_US
dc.titleUnsupervised histopathological sub-image analysis for breast cancer diagnosis using variational autoencoders, clustering, and supervised learning
dc.typeArticle

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