Detection of Tumor From Brain MRI Images Using Supervised and Unsupervised Methods

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Author(s): Kannan S. (Saveetha School of Engineering, India & Saveetha Institute of Medical and Technical Sciences, Chennai, India)and Anusuya S. (Saveetha School of Engineering, India & Saveetha Institute of Medical and Technical Sciences, Chennai, India)
Copyright: 2021
Pages: 15
Source title: AI Innovation in Medical Imaging Diagnostics
Source Author(s)/Editor(s): Kalaivani Anbarasan (Department of Computer Science and Engineering, Saveetha School of Engineering, India & Saveetha Institute of Medical and Technical Sciences, Chennai, India)
DOI: 10.4018/978-1-7998-3092-4.ch003

Keywords: Artificial Intelligence / Computer Science & IT / Medical Information Science Reference / Medical Technologies

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Abstract

Brain tumor discovery and its segmentation from the magnetic resonance images (MRI) is a difficult task that has convoluted structures that make it hard to section the tumor with MR cerebrum images, different tissues, white issue, gray issue, and cerebrospinal liquid. A mechanized grouping for brain tumor location and division helps the patients for legitimate treatment. Additionally, the method improves the analysis and decreases the indicative time. In the separation of cerebrum tumor, MRI images would focus on the size, shape, area, and surface of MRI images. In this chapter, the authors have focused various supervised and unsupervised clustering techniques for identifying brain tumor and separating it using convolutional neural network (CNN), k-means clustering, fuzzy c-means grouping, and so on.

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Detection of Tumor From Brain MRI Images Using Supervised and Unsupervised Methods

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