Effective classification of medical images using image segmentation and machine learning

Because of an increase in the frequency of encephalon tumors in each age group, the mortality rate has grown in recent years. In medical imaging, tumors are hard to see because of their complicated structure and noise, which makes it hard and time-consuming for specialists to find them. It is very important to find and pinpoint the tumor's location at an early stage, so this is very important. Medical scans can be used to look for and predict cancerous spots at different levels. These scans can be combined with segmentation and relegation methods to help doctors make an early diagnosis, which can save a lot of time. Physical tumor identification has become a challenging and time-consuming process for medical practitioners due to the intricate structure of tumors and the involution of noise in magnetic resonance (MR) imaging data. As a result, detecting and pinpointing the site of the tumour at an early stage is critical. Medical scans can be used in conjunction with segmentation and relegation procedures to deliver an accurate diagnosis at an early stage in cancer tumour locations at various levels. This research offers a system based on machine learning for segmenting and classifying MRI images for brain tumor identification. The K* classifier, Additive Regression, Bagging, Input Mapped Classifier, and Decision Table algorithms are used in this framework for image preprocessing, image segmentation, feature extraction, and classification.