Intelligent Brain Tumor Detector

Mostafa Abdelhamid; Mohammed Alhato; Ali Elmancy; Somaya Al-Maadeed; Omar El Harrouss

doi:10.1109/ISNCC58260.2023.10323954

Intelligent Brain Tumor Detector

Mostafa Abdelhamid
, Mohammed Alhato
, Ali Elmancy
, Somaya Al-Maadeed
, Omar El Harrouss

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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%.

Original language	English
Title of host publication	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350335590
DOIs	https://doi.org/10.1109/ISNCC58260.2023.10323954
Publication status	Published - 2023
Externally published	Yes
Event	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 - Doha, Qatar Duration: Oct 23 2023 → Oct 26 2023

Publication series

Name	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

Conference

Conference	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
Country/Territory	Qatar
City	Doha
Period	10/23/23 → 10/26/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

brain
image
patient
treatment
tumor

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/ISNCC58260.2023.10323954

Cite this

Abdelhamid, M., Alhato, M., Elmancy, A., Al-Maadeed, S., & El Harrouss, O. (2023). Intelligent Brain Tumor Detector. In 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 (2023 International Symposium on Networks, Computers and Communications, ISNCC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISNCC58260.2023.10323954

Intelligent Brain Tumor Detector. / Abdelhamid, Mostafa; Alhato, Mohammed; Elmancy, Ali et al.
2023 International Symposium on Networks, Computers and Communications, ISNCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Symposium on Networks, Computers and Communications, ISNCC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abdelhamid, M, Alhato, M, Elmancy, A, Al-Maadeed, S & El Harrouss, O 2023, Intelligent Brain Tumor Detector. in 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023. 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023, Doha, Qatar, 10/23/23. https://doi.org/10.1109/ISNCC58260.2023.10323954

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title = "Intelligent Brain Tumor Detector",

abstract = "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\%.",

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AU - Alhato, Mohammed

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AU - Al-Maadeed, Somaya

AU - El Harrouss, Omar

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

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M3 - Conference contribution

AN - SCOPUS:85179846020

T3 - 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

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Intelligent Brain Tumor Detector

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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