BRAIN TUMOR DETECTION BY IMAGE SEGMENTATION USING MACHINE LEARNING

Abstract

The detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated
tumor areas are significant concerns; however, a repetitive and extensive task executed by radiologists or clinical experts
relies on their expertise. Image processing concepts can imagine the various anatomical structures of the human organ.
Detection of human brain abnormal structures by basic imaging techniques is challenging. The Image Segmentation has been
proposed for brain tumor segmentation based on Machine learning techniques. The present work proposes the separation of
the whole cerebral venous system into MRI imaging with the addition of a new, fully automatic algorithm based on
structural, morphological, and relaxometry details. The segmenting function is distinguished by a high level of uniformity
between anatomy and the neighboring brain tissue. ELM is a type of learning algorithm consisting of one or more layers of
hidden nodes. Such networks are used in various areas, including regression and classification. In brain MRI images, the
probabilistic neural network classification system has been utilized for training and checking the accuracy of tumor detection
in images. The numerical results show almost 98.51% accuracy in detecting abnormal and normal tissue from brain
Magnetic Resonance images that demonstrate the efficiency of the system suggested.