Application Of Supervised Machine Learning To Characterize Brain Tissue And To Discriminate Benign Lesions, Various Grades Of Glioma And Metastasis

  • ANINDYA GANGULY, MR RESEARCH SCOLAR ,College of Health and Human Sciences, Charles Darwin University, Australia,
Keywords: Refractive Index (RI),MR Spectroscopy, Metabolites, Artificial Neural network (ANN), SVM, Error Correcting Code (ECOC), Classifier, Hyperplane, Brain Lesions.


Supervised Machine Learning (SML) an extremely powerful classifier was applied for diagnosing the various pathological lesions in the brain, like edema, multiple sclerosis MS), glioma of different grades and metastasis. MR Images may show structural changes in the brain lesions (Figure 1). MR Spectroscopy can also show change in the metabolite peaks and quantities in different disease state (Figure 2). But it is frequently difficult to diagnose the exact disease. Use of SML by two strategies like Artificial Neural Network (ANN) and Support Vector Machine (SVM) helps identifying the condition in doubtful cases. The SVM and ANN train on data sets gathered from different patients based on input variables – Refractive Index,T2 relaxation values, Choline (CHO), Apparent Diffusion Coefficient (ADC), Creatine (CR), CHO/NAA (N-acetyl aspartate), CR/NAA, LIP/LAC (Lipid/lactate), MI ( Myoinositol), CHO/CR and T2 value in the periphery of lesion. Refractive index is a vital physical parameter. After training the data, prediction by ANN and SVM show high accuracy in diagnosis. The training and testing have been carried out by Neural Tool in ANN and SVM classifier tool in MATLAB respectively.



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How to Cite
BISWAS, T. K., GANGULY, A., BANDOPADHYAY, R., & DUTTA, A. K. (2018). Application Of Supervised Machine Learning To Characterize Brain Tissue And To Discriminate Benign Lesions, Various Grades Of Glioma And Metastasis. IJRDO - Journal of Electrical And Electronics Engineering (ISSN: 2456-6055), 4(7), 01-19. Retrieved from