Brain Tumor Segmentation and Classification using Machine Learning

Brain Tumor segmentation and classification is an interesting yet challenging research domain of Biomedical Image processing. So, extracting useful information, segment an image correctly, classify an image, and accurately predict the result, not an easy task. For better segmentation and classification of any bio-medical image processing, we must focus on a good dataset, a better understanding of statistics, and better algorithms for processing image datasets. This research focuses on accurately classify tumors from MRI images using multiple Segmentation algorithms to overcome the slight chance of miss classification Error. Multiple segmentation algorithms help to generate more accurate and precise results as compared to a single segmentation algorithm. For this purpose, watershed, K-means, and Threshold segmentation algorithm are used along with SVM Classifier and finally achieve 90% above classification result at the end.