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: 2023
Pages: 12
Source title: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch053

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Imaging / Medical Information Science Reference

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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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