• Debarshee Datta
• Prof. G.G. Chiddarwar
• Aditya Gonnade
• Rahul Lalwani
• Ashmit Bhatnagar

Brain tumor segmentation is one of the computationally challenging tasks in the medical area because the target segmentation consists on the extraction process of tumor regions from images. Medical experts as per their experience do this manually which is not always obvious due to the similarity between tumor and normal tissues and the high diversity in tumors appearance. In view of this, automating medical image segmentation remains a real challenge. In this project, the work is focused on support vector machine learning method based segmentation of Magnetic Resonance Brain Images (MRI). The fundamental conceptual framework is to consider this problem as a classification problem where the aim is to distinguish between normal and abnormal pixels on the basis of several features, namely intensities and texture. More precisely, we propose to use Support Vector Machine (SVM) which is within popular and well motivating classification methods. The project also implements dose quantification which suggest the amount of radiation in the therapy required. The project has been carried on data set representing different tumor shapes, locations, sizes and image intensities has been collected from medical physics section of Radiological Physics Advisory Division, BARC.

Brain Tumor Classification, Tumor Segmentation, Dosage Calculation, Support Vector Machine(SVM)

"IDENTIFICATION AND DOSE QUANTIFICATION OF THE HUMAN BRAIN TUMOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.530-535, June-2019, Available :http://www.jetir.org/papers/JETIR1906085.pdf

"IDENTIFICATION AND DOSE QUANTIFICATION OF THE HUMAN BRAIN TUMOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp530-535, June-2019, Available at : http://www.jetir.org/papers/JETIR1906085.pdf