Brain Tumor Detection and Classification

Brain tumors vary enormously in type and aggressiveness, and identifying them accurately from MRI scans is critical for choosing the right treatment — yet manual interpretation by radiologists is time-consuming and prone to variability. Researchers are developing machine learning methods, particularly convolutional neural networks, to automate the detection, segmentation, and classification of tumors directly from imaging data, often matching or exceeding expert-level performance on benchmark datasets. Active work focuses on improving how these models extract meaningful features from scans, handle the scarcity of labeled medical images, and generalize across different scanner hardware and patient populations. A central open question is how to make these systems reliable and interpretable enough for clinicians to trust in real diagnostic settings.

Works

50,028

Total citations

375,264

Keywords

MRIBrain TumorClassificationDeep LearningConvolutional Neural NetworkFeature Extraction