https://github.com/tanushreesb/synthaid
This project detects Oral Cancer using synthetic image generation.
https://github.com/tanushreesb/synthaid
cancer-detection cnn gan html-css-javascript keras open-cv python yolo3
Last synced: about 1 month ago
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This project detects Oral Cancer using synthetic image generation.
- Host: GitHub
- URL: https://github.com/tanushreesb/synthaid
- Owner: TanushreeSB
- Created: 2025-03-22T10:13:45.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-31T10:38:41.000Z (6 months ago)
- Last Synced: 2025-03-31T11:31:42.579Z (6 months ago)
- Topics: cancer-detection, cnn, gan, html-css-javascript, keras, open-cv, python, yolo3
- Language: Python
- Homepage:
- Size: 184 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# SynthAid
- Synthetic Data Generation with GANs
- Use DCGAN to generate realistic synthetic oral cancer images.
- Augment small datasets with diverse synthetic images to improve model generalization.
- Ensure synthetic images capture detailed pathological variations for better model learning.
1. Synthetic Data Generation
DCGAN – For generating realistic synthetic oral cancer images.
Python – For training and optimizing GAN models.
TensorFlow, PyTorch – Frameworks for implementing and fine-tuning DCGAN.
3. Segmentation and Classification
U-Net – For precise localization of cancerous regions.
CNN – For classification and feature extraction.
Keras, PyTorch – For training and optimizing segmentation models.
4. Dataset Creation and Augmentation
Python – For preprocessing and combining real and synthetic images.
OpenCV – For image manipulation and augmentation
5. Model Tuning and Performance Improvement
Focal Loss, Class-Balanced Loss – To handle class imbalance and improve recall.
Active Learning – For continuous model improvement using new data.
6. Deployment and Explainability
Flask – To create a web-based interface for real-time model interaction.
