Brain Tumor Detection & Classification with Streamlit (YOLOv8)

• Built a real-time brain tumor detection and classification system using YOLOv8 on MRI scans covering four

tumor types: Glioma, Meningioma, Pituitary, and No Tumor.

• Achieved 96% overall model accuracy (mAP@50), with inference speed of 25.1ms per image, optimized for

clinical applications.

• Deployed an interactive Streamlit app allowing MRI uploads with bounding boxes, confidence scores, and tumor

information displayed in real-time.

• Tools Used: Python, YOLOv8 (Ultralytics), Streamlit, OpenCV