Brain Tumor Detection

Brain tumor detection research focuses on developing accurate and efficient methods for identifying and classifying brain tumors using medical images, primarily MRI scans. Current efforts leverage deep learning architectures, including convolutional neural networks (CNNs) like ResNet, EfficientNet, and YOLO variants, often enhanced with attention mechanisms and integrated into encoder-decoder or multi-stage frameworks for improved segmentation and classification. These advancements aim to improve diagnostic accuracy, reduce reliance on time-consuming manual analysis by radiologists, and ultimately contribute to earlier and more effective treatment of brain tumors.