Brain images application and supervised learning algorithms

A review

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Medical image processing and classification are important in medicine. Many Magnetic Resonance Images (MRI) are taken for an individual. To reduce the radiologist workload and to enable more efficiency in brain tumor detection and classification. Many Computer Aided Diagnose (CAD) systems have been developed using different segmentation methods and classification algorithms. This study synthesizes and discusses some studies and their results. A Learning Vector Quantization (L VQ) classifier is used to classify MRI images into normal and abnormal. An initial experiment consisting of normal and abnormal MRI Brain Tumor dataset from UKM Medical Center, to observe various versions of LVQ classifiers performance is conducted. From the extensive and informative studies and numerical experiments, it is expected to obtain better brain tumor classification in the future using Multi pass LVQ classifier which obtained the least standard deviation value (0.4) and the mean accuracy rate is equal to 91%.

Original languageEnglish
Pages (from-to)108-122
Number of pages15
JournalJournal of Medical Sciences (Faisalabad)
Volume14
Issue number3
DOIs
Publication statusPublished - 2014

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Learning
Brain Neoplasms
Magnetic Resonance Spectroscopy
Brain
Workload
Medicine
Efficiency
Datasets
Radiologists

Keywords

  • Brain imaging
  • Classification
  • Learning vector quantization
  • Segmentation

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Brain images application and supervised learning algorithms : A review. / Nayef, Baher H.; Sheikh Abdullah, Siti Norul Huda; Iqbal Hussain, Rizuana; Sahran, Shahnorbanun; Almasri, Abdullah H.

In: Journal of Medical Sciences (Faisalabad), Vol. 14, No. 3, 2014, p. 108-122.

Research output: Contribution to journalArticle

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