Brain Pathology Classification of MR Images Using Machine Learning Techniques

Ramaha, Nehad T. A. and Mahmood, Ruaa M. and Hameed, Alaa Ali and Fitriyani, Norma Latif and Alfian, Ganjar and Syafrudin, Muhammad (2023) Brain Pathology Classification of MR Images Using Machine Learning Techniques. Computers, 12 (8). ISSN 2073431X

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Abstract

A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97, and the deep CNN is 98. © 2023 by the authors.

Item Type: Article
Additional Information: Cited by: 3; All Open Access, Gold Open Access
Uncontrolled Keywords: machine learning; tumor segmentation; classification; feature extraction; MRI image
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Vocational School
Depositing User: Sri JUNANDI
Date Deposited: 03 Nov 2024 11:33
Last Modified: 03 Nov 2024 11:33
URI: https://ir.lib.ugm.ac.id/id/eprint/10572

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