Detection and Classification of Encephalon Tumor Using Extreme Learning Machine Learning Algorithm Based on Deep Learning Method

Abstract

The ordinary people cannot have the capability to detect and classify the brain tumor,9 so the radiologists or the clinical experts are the only person who can detect the encephalon tumor due to their practice and awareness. If the manual detection process will be carried out by the clinical experts, then they will face a lot of problem like time delay, classification accuracy etc., so for this reason, automatic encephalon tumor detection and classification process are required. In this paper, we have detected the encephalon tumor from three schemes, that is, initially, we have collected some raw data and put into the machine learning model; secondly, the classification process is done by extreme learning machine (ELM); and finally, the tumors are extracted from most commonly method gray level co-occurrence method (GLCM). For detecting the tumor region, we have used the one and only common method magnetic resonance image which have also some noises or the doted pictures are available which give a clear image to the clinical experts for detection process. In this work, the classification accuracies have produced as an approximation of 98.09% which has produced the better outcome as compared to the studies that are presented in the related work. From this work, the result is shown as an effective manner for the radiologists as it uses the deep learning method.