Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/34086
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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 radiology 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). https://doi.org/10.1186/s12938-022-01022-6 |
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. |
URI: | https://doi.org/10.1186/s12938-022-01022-6 http://hdl.handle.net/10174/34086 |
Type: | article |
Appears in Collections: | INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
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