dc.contributor.author | Hafeez, Muhammad Adeel | |
dc.contributor.author | Kayasandık, Cihan Bilge | |
dc.contributor.author | Doğan, Merve Yüşra | |
dc.date.accessioned | 2022-10-11T11:22:33Z | |
dc.date.available | 2022-10-11T11:22:33Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Hafeez, M. A., Kayasandık, C. B. ve Doğan, M. Y. (2022). Brain tumor classification using MRI images and convolutional neural networks. 30th Signal Processing and Communications Applications Conference, SIU 2022. Safranbolu, 15-18 May 2022. https://doi.org/10.1109/SIU55565.2022.9864962 | en_US |
dc.identifier.isbn | 9781665450928 | |
dc.identifier.uri | https://doi.org/10.1109/SIU55565.2022.9864962 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/9823 | |
dc.description.abstract | The brain tumor has become one of the most prominent types of cancers affecting a huge population across the globe every year. It has the lowest life expectancy rate and the risk of death is highly associated with the type, shape, and location of the tumor. The Magnetic Resonance Imaging (MRI) is a strong tool to detect different brain lesions and is extensively used by radiologists and physicians. For the early and accurate diagnosis of the brain tumor using MRI, it is important to consider automated computer-assisted diagnosis which is more flexible and efficient. In this paper, we have proposed a Convolutional Neural Network (CNN) based approach for the classification of three types of brain tumors (meningiomas, gliomas, and pituitary tumors). A publicly available dataset that contains 3064 T1-weighted brain CE-MRI images collected from 233 patients has been used in the study. We propose a 15 layers CNN model for the classification of three types of brain tumors from the mentioned dataset. We obtained an accuracy, precision, recall, and f1-score of 98.6%, 99%, 98.3%, and 98.6% from our proposed model which is higher than previously reported results. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
dc.subject | Brain Tumor | en_US |
dc.subject | CNN | en_US |
dc.subject | MRI | en_US |
dc.title | Brain tumor classification using MRI images and convolutional neural networks | en_US |
dc.type | conferenceObject | en_US |
dc.relation.ispartof | 30th Signal Processing and Communications Applications Conference, SIU 2022 | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0001-7853-1731 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/SIU55565.2022.9864962 | en_US |
dc.institutionauthor | Hafeez, Muhammad Adeel | |
dc.institutionauthor | Kayasandık, Cihan Bilge | |
dc.institutionauthor | Doğan, Merve Yüşra | |
dc.identifier.scopus | 2-s2.0-85138734005 | en_US |