🧠 YOLOTumorDetection: Fine-Tuned YOLOv8 for Brain Tumor Detection

YOLOTumorDetection is a deep learning model based on YOLOv8 (Ultralytics) and fine-tuned to detect and localize brain tumors in MRI images.
It showcases the power of transfer learning in adapting a general-purpose object detection model (pretrained on COCO) to a specialized medical imaging domain.

⚠️ Disclaimer: This model is intended for research, educational, and demonstration purposes only.
It is not approved for clinical or diagnostic use and should not be used in medical decision-making without proper validation and regulatory approval.

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🧩 Model Details

Key Features:

• Detects and localizes tumorous regions in brain MRI scans
• Fine-tuned from YOLOv8 pretrained on COCO dataset
• Produces bounding boxes with confidence scores
• Supports real-time inference through web deployment
• Deployed as an interactive Streamlit app and hosted on Hugging Face Spaces

Technologies Used:

• Ultralytics YOLOv8 for object detection
• Transfer learning on a labeled brain tumor MRI dataset
• PyTorch backend with GPU acceleration
• Streamlit for interactive web deployment
• Hugging Face Spaces for open model hosting

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• Developed by: Rawan Alwadeya
• Model Type: Object Detection (YOLOv8)
• Language(s): N/A (Image model)
• License: MIT

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🎯 Intended Uses

This model can be applied to:

• Research in AI-based medical image analysis
• Educational projects demonstrating deep learning in healthcare
• Prototype development for tumor localization in MRI images
• Healthcare AI demonstrations for diagnostic support systems

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📊 Performance

The fine-tuned YOLOv8 model achieved the following metrics on the test set:

• Precision: 91.16%
• Recall: 96.87%
• mAP@50: 96.63%

These metrics indicate strong tumor localization performance and high detection reliability, even with varied MRI samples.

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🚀 Deployment

• Hugging Face Repo: YOLOTumorDetection
• Demo Video: View on LinkedIn

Users can upload MRI images and instantly view bounding boxes highlighting detected tumor regions, supporting faster and more explainable medical image interpretation.

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👩‍💻 Author

Rawan Alwadeya
AI Engineer | Generative AI Engineer | Data Scientist

• 📧 Email: [email protected]
• 🌐 LinkedIn Profile

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🧪 Example Usage

from ultralytics import YOLO
import cv2

# Load model from Hugging Face Hub
model = YOLO("RawanAlwadeya/YOLOTumorDetection")

# Run inference on an MRI image
results = model("brain_mri_example.jpg")

# Visualize results
for r in results:
    im_array = r.plot()  # BGR image with predictions
    cv2.imshow("Brain Tumor Detection", im_array)
    cv2.waitKey(0)
    cv2.destroyAllWindows()