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The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas

D. Alis, Ömer Bağcılar, Yeseren Deniz Senli, Cihan İşler, Mert Yergin, Naci Koçer, Civan Işlak, Osman Kızılkılıç

2020Clinical Radiology26 citationsDOI

Topics & Concepts

Fluid-attenuated inversion recoveryMedicineMagnetic resonance imagingReproducibilityArtificial intelligenceReceiver operating characteristicNuclear medicinePattern recognition (psychology)Grading (engineering)Artificial neural networkHistogramRadiologyComputer scienceStatisticsMathematicsInternal medicineCivil engineeringImage (mathematics)EngineeringGlioma Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingBrain Tumor Detection and Classification
The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas | Litcius