TY - GEN
T1 - Intelligent Brain Tumor Detector
AU - Abdelhamid, Mostafa
AU - Alhato, Mohammed
AU - Elmancy, Ali
AU - Al-Maadeed, Somaya
AU - El Harrouss, Omar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - A 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%.
AB - A 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%.
KW - brain
KW - image
KW - patient
KW - treatment
KW - tumor
UR - https://www.scopus.com/pages/publications/85179846020
UR - https://www.scopus.com/pages/publications/85179846020#tab=citedBy
U2 - 10.1109/ISNCC58260.2023.10323954
DO - 10.1109/ISNCC58260.2023.10323954
M3 - Conference contribution
AN - SCOPUS:85179846020
T3 - 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
BT - 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
Y2 - 23 October 2023 through 26 October 2023
ER -
