Design and implementation of a computer-aided diagnosis system for brain tumor classification

Mahmoud Khaled Abd-Ellah; Ali Ismail Awad; Ashraf A.M. Khalaf; Hesham F.A. Hamed

doi:10.1109/ICM.2016.7847911

Design and implementation of a computer-aided diagnosis system for brain tumor classification

Mahmoud Khaled Abd-Ellah
, Ali Ismail Awad
, Ashraf A.M. Khalaf
, Hesham F.A. Hamed

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

66 Citations (Scopus)

Abstract

Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.

Original language	English
Title of host publication	ICM 2016 - 28th International Conference on Microelectronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	73-76
Number of pages	4
ISBN (Electronic)	9781509057214
DOIs	https://doi.org/10.1109/ICM.2016.7847911
Publication status	Published - Jul 2 2016
Externally published	Yes
Event	28th International Conference on Microelectronics, ICM 2016 - Giza, Egypt Duration: Dec 17 2016 → Dec 20 2016

Publication series

Name	Proceedings of the International Conference on Microelectronics, ICM
Volume	0

Conference

Conference	28th International Conference on Microelectronics, ICM 2016
Country/Territory	Egypt
City	Giza
Period	12/17/16 → 12/20/16

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

benign tumor
Brain tumor
DWT
K-means
malignant tumor
MRIs
PCA
SVM
tumor classification
tumor detection

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ICM.2016.7847911

Cite this

Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2016). Design and implementation of a computer-aided diagnosis system for brain tumor classification. In ICM 2016 - 28th International Conference on Microelectronics (pp. 73-76). Article 7847911 (Proceedings of the International Conference on Microelectronics, ICM; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICM.2016.7847911

Design and implementation of a computer-aided diagnosis system for brain tumor classification. / Abd-Ellah, Mahmoud Khaled; Awad, Ali Ismail; Khalaf, Ashraf A.M. et al.
ICM 2016 - 28th International Conference on Microelectronics. Institute of Electrical and Electronics Engineers Inc., 2016. p. 73-76 7847911 (Proceedings of the International Conference on Microelectronics, ICM; Vol. 0).

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

Abd-Ellah, MK, Awad, AI, Khalaf, AAM & Hamed, HFA 2016, Design and implementation of a computer-aided diagnosis system for brain tumor classification. in ICM 2016 - 28th International Conference on Microelectronics., 7847911, Proceedings of the International Conference on Microelectronics, ICM, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 73-76, 28th International Conference on Microelectronics, ICM 2016, Giza, Egypt, 12/17/16. https://doi.org/10.1109/ICM.2016.7847911

Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HFA. Design and implementation of a computer-aided diagnosis system for brain tumor classification. In ICM 2016 - 28th International Conference on Microelectronics. Institute of Electrical and Electronics Engineers Inc. 2016. p. 73-76. 7847911. (Proceedings of the International Conference on Microelectronics, ICM). doi: 10.1109/ICM.2016.7847911

Abd-Ellah, Mahmoud Khaled ; Awad, Ali Ismail ; Khalaf, Ashraf A.M. et al. / Design and implementation of a computer-aided diagnosis system for brain tumor classification. ICM 2016 - 28th International Conference on Microelectronics. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 73-76 (Proceedings of the International Conference on Microelectronics, ICM).

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title = "Design and implementation of a computer-aided diagnosis system for brain tumor classification",

abstract = "Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.",

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N2 - Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.

AB - Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.

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Design and implementation of a computer-aided diagnosis system for brain tumor classification

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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