Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6379
Title: Classification of Brain Tumors via Deep Learning Models
Authors: Daglı, Kaya
Eroğul, Osman
Keywords: Accuracy
Brain Tumor
Classification
Deep Learning
Magnetic Resonance Images
Time Consumption
Issue Date: 2020
Publisher: IEEE
Source: Medical Technologies National Congress (TIPTEKNO) -- NOV 19-20, 2020 -- ELECTR NETWORK
Abstract: Brain tumors threathen human health significantly. Misdiagnosis of these tumors decrease effectiveness of decisions for intervention and patient's state of health. The conventional method to differentiate brain tumors is by the inspection of magnetic resonance images by clinicians. Since there are various types of brain tumors and there are many images that clinicians should examine, this method is both prone to human errors and causes excessive time consumption. In this study, the most common brain tumor types; Glioma, Meningioma and Pituitary are classified using deep learning models. While the main objective of this study is to have a high rate of accuracy, the time spent is also examined. The aim of this study is to ease clinicians work load and have a time efficient classification system. The system which has been built has an accuracy up to 90 %.
URI: https://hdl.handle.net/20.500.11851/6379
ISBN: 978-1-7281-8073-1
Appears in Collections:Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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