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.

2. 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.

3. Dataset Creation and Augmentation Python – For preprocessing and combining real and synthetic images. OpenCV – For image manipulation and augmentation

4. 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.

5. Deployment and Explainability Flask – To create a web-based interface for real-time model interaction.