Abstract: 

The death rate has increased in recent years due to the rising prevalence of encephaloma tumors across all age brackets. Because of their complex structure and background noise, tumors are difficult to detect in medical imaging and require a great deal of time and effort on the part of professionals. This is crucial since locating the tumor early on is key to successful treatment. Scans can detect and even forecast the presence of cancer at a variety of stages. A combination of these scans with segmentation and relegation techniques can aid in a rapid diagnosis, saving valuable time for the treating physician. Due to the complex nature of tumors and the gradual evolution of noise in MR imaging data, physical tumor identification has become a complicated and time-consuming process for medical professionals. Hence, early detection and localization of the tumor site is essential. Using segmentation and relegation techniques, medical imaging can pinpoint cancer tumors at multiple stages for a precise diagnosis. This study presents a machine learning-based method for automatically segmenting and labelling MRI scans of the brain to help in the detection of malignant growths. In addition, this framework employs a number of machine learning algorithms for tasks including image pre-processing, segmentation, feature extraction, and classification, including Nave Bayes, Nearest Neighbours, and Decision Table.