This repository implements a tumor detection system using YOLOv11, an advanced object detection model. The system aims to detect and classify tumors in medical images, providing faster and more accurate diagnostic support.
- State-of-the-Art Detection: Built with YOLOv11 for real-time and high-accuracy tumor detection.
- Custom Dataset: Trained on a well-annotated medical image dataset.
- Versatile Input: Supports various image formats (DICOM, PNG, JPEG).
- Detailed Output: Annotated results with bounding boxes and confidence scores.
Ensure you have the following installed:
- Python 3.8 or higher
- CUDA-enabled GPU (for optimal training and inference performance)
- Required Python libraries (see
requirements.txt)