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 proceedingConference contribution

53 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 languageEnglish
Title of host publicationICM 2016 - 28th International Conference on Microelectronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-76
Number of pages4
ISBN (Electronic)9781509057214
DOIs
Publication statusPublished - Jul 2 2016
Externally publishedYes
Event28th International Conference on Microelectronics, ICM 2016 - Giza, Egypt
Duration: Dec 17 2016Dec 20 2016

Publication series

NameProceedings of the International Conference on Microelectronics, ICM
Volume0

Conference

Conference28th International Conference on Microelectronics, ICM 2016
Country/TerritoryEgypt
CityGiza
Period12/17/1612/20/16

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

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