Article

Supervised Classification of Normal and Tumorous Brain MR Images Using Machine Learning Schemes

2020-07-29

Abstract excerpt

Manual interpretation of these huge amounts of image volumes are susceptible to inter-reader variability and human error. Thus, accurate automated CAD scheme is highly desirable in clinical pathological diagnosis. In this research, plethora of machine learning paradigms (e.g. feature extraction, dimensionality reduction and supervised classification methods) were explored, evaluated, compared and analyzed to ident...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f4409247-a8c5-5b1e-b570-96c366567eb8
DOI: 10.20944/preprints202007.0688.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Supervised Classification of Normal and Tumorous Brain MR Images Using Machine Learning SchemesDOI 10.20944/preprints202007.0688.v1Read

Comparative Evaluation Of Machine Learning Classifiers For Brain Tumor DetectionSemantic proximity Read

Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.Semantic proximity Read

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation statusSemantic proximity Read

Training and testing a CNN-based engine for brain MRI scan classification and segmentationSemantic proximity Read

Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning Based Prediction Models in Distinguishing Glioblastomas from Single Brain MetastasesSemantic proximity Read

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.Semantic proximity Read

Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns.Semantic proximity Read

End-to-End Machine Learning based Discrimination of Neoplastic and Non-neoplastic Intracerebral Hemorrhage on Computed TomographySemantic proximity Read

Predicting MGMT Methylation in Glioblastoma for Informed Clinical Decisions: An AI-Driven Approach in Resource-Limited SettingsSemantic proximity Read

Evaluating Machine Learning Classifiers in Breast Cancer: Non-Linear Contributions of MR Diffusion-Perfusion Features to Molecular-based Prognostic StratificationSemantic proximity Read

Optimizing machine learning performance for medical imaging analyses in low-resource environments: The prospects of CNN-based Feature ExtractorsSemantic proximity Read

Differential Diagnosis of Cognitive Disorders using Deep Learning Techniques based on Neuroimaging DataSemantic proximity Read