Brain Tumour Detection from Mri Images Using Enhanced Extreme Machine Learning Probabilistic Scaling

Abstract

The development of aberrant cells, some of which may develop into cancer, results in a brain tumour. Magnetic resonance imaging (MRI) scans frequently reveal brain malignancies. MRI scans are used to identify the abnormal tissue growth in the brain. In several research publications, algorithms for machine learning and deep learning are used to detect brain tumours. It can be used to identify brain tumours quickly and accurately in MRI scans, which makes it simpler to treat patients. These forecasts also help the radiologist act quickly. In the suggested work, preprocessing, segmentation, feature extraction, and classification are all included. An MRI brain image’s undesirable pixels are removed using the Wiener filter during the preprocessing stage. To divide up the data, we applied the fuzzy means clustering (FCM) algorithm. In the second stage, the characteristics of the MRI’s GLCM are extracting the features from the image associated with the MRI brain image. An enhanced extreme learning machine probabilistic scaling is applied in the classification step to categorize the prevailing output image and the interrogation image. The results demonstrate how effective and reliable the suggested methodology is when compared to other recent studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.