Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques Sahan M. Vijithananda, Mohan L. Jayatilake, Badra Hewavithana, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1186157/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted 7 You are reading this latest preprint version Show more versions Abstract 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. Conclusion: 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. Magnetic Resonance Imaging Diffusion Weighted Imaging Apparent Diffusion Coefficient Brain Tumor Classification ANOVA f-test feature selection Machine Learning Random Forest Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 2 posted Editorial decision: Major revision 01 Feb, 2022 Reviews received at journal 15 Jan, 2022 Reviewers agreed at journal 13 Jan, 2022 Reviewers invited by journal 13 Jan, 2022 Editor assigned by journal 12 Jan, 2022 Submission checks completed at journal 11 Jan, 2022 First submitted to journal 07 Jan, 2022 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1186157","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2022-01-06 21:27:41","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":79682489,"identity":"189c0b63-a2fb-4aa8-919a-94e9add81edf","order_by":0,"name":"Sahan M. Vijithananda","email":"","orcid":"","institution":"University of Peradeniya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sahan","middleName":"M.","lastName":"Vijithananda","suffix":""},{"id":79682490,"identity":"24e89cb9-8895-4118-a5e1-4b2ce69fd1e1","order_by":1,"name":"Mohan L. 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Silva","email":"","orcid":"","institution":"National Hospital of Sri Lanka","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anil","middleName":"D.","lastName":"Silva","suffix":""},{"id":79682497,"identity":"0f0705d6-4cf6-4552-81db-4d16fdd5df48","order_by":8,"name":"Karuna D. Dissanayake","email":"","orcid":"","institution":"National Hospital of Sri Lanka","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karuna","middleName":"D.","lastName":"Dissanayake","suffix":""}],"badges":[],"createdAt":"2021-12-19 17:59:04","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1186157/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1186157/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17710236,"identity":"81bdfc1f-dcf4-448f-9e1d-e2990de9db90","added_by":"auto","created_at":"2022-01-27 18:22:31","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":503361,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1186157/v2_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1186157/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Magnetic Resonance Imaging, Diffusion Weighted Imaging, Apparent Diffusion Coefficient, Brain Tumor Classification, ANOVA f-test feature selection, Machine Learning, Random Forest","lastPublishedDoi":"10.21203/rs.3.rs-1186157/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1186157/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: 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. \u003c/p\u003e\u003cp\u003eMethods: 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. \u003c/p\u003e\u003cp\u003eResults: 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. \u003c/p\u003e\u003cp\u003eConclusion: 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.\u003c/p\u003e","manuscriptTitle":"Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-01-27 18:22:19","doi":"10.21203/rs.3.rs-1186157/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-02-01T09:28:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-01-15T20:28:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"fd36cf4a-3e57-41cb-92b3-4119618c5dc4","date":"2022-01-13T08:27:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-13T07:55:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-12T20:01:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-01-11T06:06:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioMedical Engineering OnLine","date":"2022-01-07T08:56:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec6b0a54-2130-496e-a43a-48c1fce8d7ab","owner":[],"postedDate":"January 27th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-07-13T14:59:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-27 18:22:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1186157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1186157","identity":"rs-1186157","version":["v2"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}