Perceptive Proposition of Combined Boosted Algorithm for Brain Tumor Segmentation

In recent days, brain tumor detection through Magnetic Resonance Imaging (MRI) is becoming broad and current interest because it is a very challenging task even, in today's modern medical image processing research. Earlier, many researchers used a variety of algorithms to segment the tumor from MRI images. However, this research paper outlines a hybrid approach detecting brain tumor through MRI image segmentation for better accuracy than earlier techniques, where both region-based and supervised classifiers-based techniques are combined together. The problem of over-segmentation has been minimized. The combined feature extraction technique has also added a new concept in our system. In addition, the paper concludes with the status checking of the tumor & provides a necessary diagnosis of brain tumor. Lastly, we compare our proposed model with other techniques and get a far better result.