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dc.contributor.authorHancer, Emrah
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-13T06:25:55Z
dc.date.available2023-03-13T06:25:55Z
dc.date.issued2023-01-20
dc.identifier.citationEmrah Hancer, Abdulhamit Subasi, Chapter 13 - Diagnosis of breast cancer from histopathological images with deep learning architectures, Editor(s): Abdulhamit Subasi, In Artificial Intelligence Applications in Healthcare&Medicine, Applications of Artificial Intelligence in Medical Imaging, Academic Press, 2023, Pages 321-332, ISBN 9780443184505, https://doi.org/10.1016/B978-0-443-18450-5.00002-5. (https://www.sciencedirect.com/science/article/pii/B9780443184505000025)en_US
dc.identifier.isbn9780443184505en_US
dc.identifier.doihttps://doi.org/10.1016/B978-0-443-18450-5.00002-5en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/589
dc.description.abstractBreast cancer is one of the most common cancer types among women worldwide. If not treated in earlier stages, it may be fatal. Therefore early diagnosis of breast cancer can minimize the human life risk. Mammograms and ultrasound imaging technologies play a crucial role to detect intraductal papillomas. However, the determination process of intraductal papillomas requires histopathological image analysis which may be mostly time-consuming, subjective, and tedious if carried out manually by the experts. To cover issue, computer-aided diagnosis (CAD) systems came into consideration. However, earlier CAD systems could not achieve significant improvement in the diagnosis process and their usage of them did not become widespread for more than a decade. Since deep learning has made so many significant advances in a wide variety of image applications, CAD systems that use its principles perform as well as the experts in stand-alone mode, and even perform better when used in support mode. In this chapter, we utilized various deep learning architectures for the detection process of breast cancer on the invasive ductal carcinoma (IDC) dataset which is one of the most popular and remarkable datasets in this field. According to the results, the pretrained VGG16 and MobileNet architectures obtain the best detection performance, reaching nearly 92% classification accuracy.en_US
dc.publisherAcademic Pressen_US
dc.subjectBreast Canceren_US
dc.subjectHistopathological Imagesen_US
dc.subjectDeep Learning (DL)en_US
dc.subjectConvolutional Neural Networks (CNNs)en_US
dc.subjectTransfer Learning (TL)en_US
dc.titleDiagnosis of breast cancer from histopathological images with deep learning architecturesen_US
dc.source.booktitleApplications of Artificial Intelligence in Medical Imaging.en_US
dc.source.volumeArtificial Intelligence Applications in Healthcare&Medicine;
dc.source.pages321-332en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorHancer, Emrah


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