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dc.contributor.authorEmrah Hancer
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2024-03-12T10:36:07Z
dc.date.available2024-03-12T10:36:07Z
dc.date.issued2023-01-01
dc.identifier.doihttps://doi.org/10.1016/B978-0-443-18450-5.00002-5en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1490
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.titleDiagnosis of breast cancer from histopathological images with deep learning architecturesen_US
dc.source.booktitleApplications of Artificial Intelligence in Medical Imagingen_US
dc.source.pages321-332en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labNAen_US
dc.subject.KSAICTen_US
dc.contributor.ugstudentNAen_US
dc.contributor.alumnaeNAen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.pgstudentNAen_US
dc.contributor.firstauthorEmrah Hancer


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