Please use this identifier to cite or link to this item:

Title: Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques
Authors: Vijithananda, Sahan
Jayatilake, Mohan
Hewavithana, Badra
Gonçalves, Teresa
Rato, Luis
Weerakoon, Bimali
Kalupahana, Tharindu
Silva, Anil
Dissanayake, Karuna
Keywords: Images
Brain tumor classification
magnetic resonance imaging
Machine Learning
Apparent diffusion coefficient
Diffusion weighted imaging
Random forest
ANOVA f-test feature selection
Issue Date: Aug-2022
Publisher: springer Nature
Citation: Vijithananda, S.M., Jayatilake, M.L., Hewavithana, B. et al. Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques. BioMed Eng OnLine 21, 52 (2022).
Abstract: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

Files in This Item:

File Description SizeFormat
2022_Feature extraction from MRI ADC images (MPhil paper 1).pdf1.64 MBAdobe PDFView/Open
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois