Enhanced Brain Tumor Detection and Segmentation Using SVM and DS

Automatic statistical classification-based segmentation of MR images of healthy brains, including initialization and geometric restrictions based on an atlas prior. The most recent extension, which was successfully used to detect multiple sclerosis lesions, identifies brain lesions as outliers. However, because of their substantial size and/or overlapping intensities with normal tissue, brain tumors cannot be easily characterized as intensity outliers. One of the most dangerous and potentially fatal illnesses that may impact people of any age is a brain tumor. For brain cancers to be effectively treated and managed, early identification and diagnosis are essential. In the medical area, the DS algorithm and Support Vector Machines (SVM) have been used to help detect brain cancers. This method analyzes patient data and predicts whether brain tumors are present by combining SVM with data structures. All things considered, the SVM and data structure method can offer a useful tool for brain tumor diagnosis and detection, perhaps enhancing patient outcomes and quality of life.