YOLOv11 + SAM2 for Brain Tumor Segmentation | Machine Learning Project Demo

🎓 Arch Technologies Machine Learning Internship Project
🔬 Project Title: Brain Tumor Segmentation using YOLOv11 and SAM2
👩‍💻 Intern: Sana Mehdi
📅 Task: Intermediate – Task 7 (Instance Segmentation with SAM2)

🧠 In this video, I demonstrate my implementation of a brain tumor segmentation system as part of my ML internship at Arch Technologies. The system combines YOLOv11 for object detection and Segment Anything Model v2 (SAM2) for precise instance segmentation of brain tumors in MRI images.

🚀 Key Components:
Dataset: Brain tumor MRI images (glioma, meningioma, pituitary, no tumor)
Model 1: YOLOv11 – high-speed real-time object detection
Model 2: SAM2 – automatic mask generation and fine-grained segmentation
Platform: Google Colab
Languages & Libraries: Python, PyTorch, OpenCV, PIL
📌 Output:
Automatic segmentation masks
Visual overlays on tumor images
Saved segmented masks for further analysis
📂 GitHub Repository:
🔗 https://github.com/SanaMehdi/brain_tumor_segmentation_yolo_ml

📄 Project Report: Included in the GitHub repo
📌 README: Full explanation of implementation and results

💼 Internship Program: Arch Technologies
📊 Category B – Intermediate Level

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Видео YOLOv11 + SAM2 for Brain Tumor Segmentation | Machine Learning Project Demo канала Sana Mehdi