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Brain Tumour Disease Pattern Identification from Metabolites in Magnetic Resonance Spectroscopy Graph using Data Mining Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Meghana Nagori, Madhuri S. Joshi

Meghana Nagori and Madhuri S Joshi. Article: Brain Tumour Disease Pattern Identification from Metabolites in Magnetic Resonance Spectroscopy Graph using Data Mining Techniques. International Journal of Computer Applications 138(13):19-22, March 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Meghana Nagori and Madhuri S. Joshi},
	title = {Article: Brain Tumour Disease Pattern Identification from Metabolites in Magnetic Resonance Spectroscopy Graph using Data Mining Techniques},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {138},
	number = {13},
	pages = {19-22},
	month = {March},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


One of the significant applications of image classification is the medical field in which the abnormal brain tumor images are categorized prior to treatment planning. Accurate identification of the type of the brain abnormality is highly essential since the treatment planning is different for all the brain abnormalities. Any false detection may lead to a wrong treatment which ultimately leads to fatal results. By employing the Magnetic Resonance Spectroscopy (MRS) graph and thereby extracting the values of the metabolites from the graph one can classify the tumor based on the values of metabolites. The aim of this research is to identify brain tumour disease pattern from MRS images to perform differential diagnosis. The authors have employed the use of the Naïve –Bayes and J48 classifier for identification of the disease pattern from the three metabolite ratios.


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MRS, Metabolites, Brain tumour, Naïve-Bayes, Confusion Matrix, Cross-Validation, J48