Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques.
Background: 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 Coeffic...
| Publicado en: | BioMedical Engineering OnLine Vol. 21; no. 1; pp. 1 - 22 |
|---|---|
| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
8/1/2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159162131&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159162131 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 8/1/2022 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 159162131 159162131 NLM35915448 10.1186/s12938-022-01022-6 NLM35915448 159162131 ppf: 1 ppct: 21 formats: tig: atl: Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques. aug: au: Vijithananda, Sahan M. Jayatilake, Mohan L. Hewavithana, Badra Gonçalves, Teresa Rato, Luis M. Weerakoon, Bimali S. Kalupahana, Tharindu D. Silva, Anil D. Dissanayake, Karuna D. affil: Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka sug: subj: Brain Neoplasms Prospective Studies Magnetic Resonance Imaging Retrospective Design Brain Scales ab: Background: 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.Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.Conclusions: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|