Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145755
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dc.contributor.authorWang, Yongjunen_US
dc.contributor.authorLei, Baiyingen_US
dc.contributor.authorElazab, Ahmeden_US
dc.contributor.authorTan, Ee-Lengen_US
dc.contributor.authorWang, Weien_US
dc.contributor.authorHuang, Fanglinen_US
dc.contributor.authorGong, Xuehaoen_US
dc.contributor.authorWang, Tianfuen_US
dc.date.accessioned2021-01-07T02:43:56Z-
dc.date.available2021-01-07T02:43:56Z-
dc.date.issued2020-
dc.identifier.citationWang, Y., Lei, B., Elazab, A., Tan, E.-L., Wang, W., Huang, F., . . . Wang, T. (2020). Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning. IEEE Access, 8, 27779-27792. doi:10.1109/ACCESS.2020.2964276en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttps://hdl.handle.net/10356/145755-
dc.description.abstractHistopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. However, it has limited efficiency and thus the detection of breast cancer is still an open issue in medical image analysis. To improve the early diagnostic accuracy of breast cancer and reduce the workload of doctors, we devise a classification framework based on histology images by combining deep learning with machine learning methodologies in this paper. Specifically, we devise a multi-network feature extraction model by using pre-trained deep convolution neural networks (DCNNs), develop an effective feature dimension reduction method and train an ensemble support vector machine (E-SVM). First, we preprocess the histological images via scale transformation and color enhancement methods. Second, the multi-network features are extracted by using four pre-trained DCNNs (e.g., DenseNet-121, ResNet-50, multi-level InceptionV3, and multi-level VGG-16). Third, a feature selection method via dual-network orthogonal low-rank learning (DOLL) is further developed for performance boosting and overfitting alleviation. Finally, an E-SVM is trained via fused features and voting strategy to perform the classification task, which classifies the images into four classes (i.e., benign, in situ carcinomas, invasive carcinomas, and normal). We evaluate the proposed method on the public ICIAR 2018 Challenge dataset of histology images of breast cancer and achieve a high classification accuracy of 97.70%. Experimental results show that our method can achieve quite promising performance and outperform state-of-the-art methods.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Accessen_US
dc.rights© 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleBreast cancer image classification via multi-network features and dual-network orthogonal low-rank learningen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/ACCESS.2020.2964276-
dc.description.versionPublished versionen_US
dc.identifier.volume8en_US
dc.identifier.spage27779en_US
dc.identifier.epage27792en_US
dc.subject.keywordsBreast Cancer Image Classificationen_US
dc.subject.keywordsDeep Convolutional Neural Networken_US
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