Brain Tumor Detection and Classification

Brain tumors vary enormously in type and aggressiveness, and distinguishing between them accurately is critical for choosing the right treatment and predicting patient outcomes. Researchers are developing automated systems that analyze MRI scans using deep learning techniques — particularly convolutional neural networks — to classify tumors by type and grade faster and more reliably than traditional manual review. A central challenge is training these models to generalize across the wide variability in tumor appearance, scanner hardware, and imaging protocols found in real clinical settings. Active work focuses on improving how networks segment and extract meaningful features from scans, and on making model decisions interpretable enough that clinicians can trust and act on them.

Works

48,404

Total citations

353,827

Keywords

MRIBrain TumorClassificationDeep LearningConvolutional Neural NetworkFeature Extraction