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dc.contributor.authorKalkan, Soner Can
dc.contributor.authorSahingoz, Ozgur Koray
dc.date.accessioned2020-02-19T14:05:49Z
dc.date.available2020-02-19T14:05:49Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-1013-4
dc.identifier.urihttps://hdl.handle.net/11413/6246
dc.description.abstractAs one of the most life-threatening disease in the tropical and warmer-climate countries, Malaria affects not only animals but also humans who can be infected by only a single bite from a mosquito. Although this disease is wiped out in high-income countries, as a result of traveling people, it can even emerge in all part of the world. World Health Organization announced that more than 400,000 people are expected to die due to this illness. However, it is a curable and preventable disease, if early detection is possible. Traditionally, Pathologists diagnosed this disease manually by using microscope which is a time-consuming process in our computerized world, and this model depends on the experience of the Pathologists, which is a critical problem in rural areas. Therefore, in recent years detection of Malaria using computerized image analysis which is trained using some dynamic learning mechanism has gained increasing importance. In this paper, we proposed an image processing-based Malaria detection system which is trained by deep learning. We used relatively big data for increasing the accuracy of the system, and the reached accuracy showed that the proposed system has an outstanding classification rate that can be used in real-world detection.
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectDeep Learning
dc.subjectBig Data
dc.subjectMalaria
dc.subjectSlide Images
dc.subjectDerin Öğrenme
dc.subjectBüyük Veri
dc.subjectSıtma
dc.subjectSlayt Görüntüleri
dc.titleDeep learning based classification of malaria from slide images
dc.typeBook chaptertr_TR
dc.relation.journal2019 SCIENTIFIC MEETING ON ELECTRICAL-ELECTRONICS & BIOMEDICAL ENGINEERING AND COMPUTER SCIENCE (EBBT)tr_TR
dc.identifier.scopus2-s2.0-85068545311


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States