AN EFFICIENT AND EXTREME LEARNING MACHINE FOR AUTOMATED DIAGNOSIS OF BRAIN TUMOR

An efficient automated system is needed to detect the tumor affected and non-effected region of human brain. Though there are traditional models which are already available for the said purpose, But they are facing the problem of excessive segmentation, greater time consumption, and high error rate and over fitting problem.The suggested method with the aid of preprocessing procedures enhances the contrast and quality of the input MRI images. From the pre-processed images, both texture and statistical characteristics are taken out for the subsequent classification process. Further with the aid of the Rapid Sine Cosine Swarm Optimisation method, dimensionality reduction has been accomplished. This has lowered training times and improved model accuracy. To precisely classify healthy and non-healthy images extreme learning algorithm is employed. Finally, to locate the exact tumor affected part of the image auto encoder-based segmentation is used. The proposed model is executed on the BRATS dataset and assessed utilizing a range of performance metrics, including accuracy, recall, F1-score, and precision, which yielded results of 98.93%, 99.21%, 97.67%, and 96.17% respectively, based on the training dataset.