Medical infection detection and segmentation are currently trending subjects in computer vision because to their growing importance in fields like medical imaging. This is because the prevalence of medical-related infections is on the rise. Several image processing and machine learning technologies have been produced for the efficient detection and categorization of disease symptoms in the modern world as a result of breakthroughs in the field of information technology. In this paper, we develop a support vector regression model to segment the brain tumor regions. The study considers several images from the datasets to train the model and then test it to reveal the accuracy of segmentation. The model is simulation in python over various test images and the results shows an improved accuracy rate over various extracted features.

Support Vector Regression, Segmentation, Brain Tumor, Medical Infection