Machine Learning Approach for MRI Brain Tumor Detection using Regularized Extreme Learning

One of the most useful imaging methods for finding brain tumors is magnetic resonance imaging. Among the deadliest human diseases, brain tumors rank high. Brain MRI is a lifesaver for radiologists when it comes to diagnosing and treating patients with brain tumors. Radiologist have utilized magnetic resonance imaging (MRI), a relatively new imaging method, to visualize the anatomy and physiology of the human body. Whether a tumor is benign or malignant, it can be utilized to characterize it and track its course. The output weights may be efficiently derived using the suggested GELM model, which shares a closed form solution with the classic DCN, RCNN, UNet. The numerical results show that the suggested method is effective in identifying abnormal and normal tissue from brain MRI images with a higher level of accuracy (94.54%), sensitivity (93.34%), and specificity (93.47%).