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AuthorAbdelhamid, Mostafa
AuthorAlhato, Mohammed
AuthorElmancy, Ali
AuthorAl-Maadeed, Somaya
AuthorEl Harrouss, Omar
Available date2024-10-14T08:51:47Z
Publication Date2023-01-01
Publication Name2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
Identifierhttp://dx.doi.org/10.1109/ISNCC58260.2023.10323954
CitationAbdelhamid, M., Alhato, M., Elmancy, A., Al-Máadeed, S., & El Harrouss, O. (2023, October). Intelligent Brain Tumor Detector. In 2023 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.‏
ISBN[9798350335590]
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179846020&origin=inward
URIhttp://hdl.handle.net/10576/60098
AbstractA major challenge in brain tumor treatment planning is determination of the tumor extent. Brain tumor disease can be identified with imaging techniques such as MRI. The images produced by an MRI scan can provide a clear view of the brain's internal structures, including the presence of any abnormal growths or tumors. Tumors can be seen on the images as areas of abnormal tissue that have a different signal intensity from normal brain tissue. In addition, the MRI images can also provide information about the size, shape, location, and characteristics of the tumor, such as its blood flow and whether it is solid or cystic. This information can be very helpful in determining the best course of treatment for the patient. The main objective of this paper is to recognize the existence of tumors in the brain from MRI images using machine learning techniques. Our results show that the k-nearest approach is capable of precisely identifying brain cancers with more than 97%.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectbrain
image
patient
treatment
tumor
TitleIntelligent Brain Tumor Detector
TypeConference Paper
Pagination1-6
dc.accessType Abstract Only


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