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Brain Disorders Identification by Machine Learning Classifiers

Journal of physics. Conference series, 2023-03, Vol.2466 (1), p.12036 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2466/1/012036

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  • Title:
    Brain Disorders Identification by Machine Learning Classifiers
  • Author: Venkataramanaiah, B. ; Sambath kumar, K. ; Giridhar Reddy, K
  • Subjects: Brain ; Brain cancer ; Computed tomography ; Error reduction ; Human error ; Identification methods ; Illnesses ; Image classification ; Machine learning ; Magnetic resonance imaging ; Medical imaging ; Physics ; Support vector machines ; Tumors
  • Is Part Of: Journal of physics. Conference series, 2023-03, Vol.2466 (1), p.12036
  • Description: Abstract Medical imaging like MRI and CT scan images are crucial for accurately diagnosing human brain disease. The traditional method for tumour analysis relies on the radiologist or physician visually inspecting the specimen, which can result in some incorrect classifications when a large number of MRI pictures need to be processed. An automated intelligent classification system is suggested that requires picture categorization in order to reduce human mistake rates. One of the illnesses that kills the majority of individuals worldwide is the brain tumour. If the tumour is accurately anticipated at an early stage, the likelihood that someone would survive can be increased. The human brain is studied using the magnetic resonance imaging (MRI) method to identify illnesses. In this project, Support Vector Machines (SVM)-based classification approaches are suggested and implemented to classify brain images; DWT will extract features from MRI images. The primary goal of this research is to provide a superior result, which is higher accuracy and reduced error rates for SVM-based MRI brain tumour prediction.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2466/1/012036
  • Source: IOP Publishing Free Content
    IOPscience (Open Access)
    GFMER Free Medical Journals
    ProQuest Central
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