- Synthetic Data Generation with GANs
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Use DCGAN to generate realistic synthetic oral cancer images.
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Augment small datasets with diverse synthetic images to improve model generalization.
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Ensure synthetic images capture detailed pathological variations for better model learning.
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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.
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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.
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Dataset Creation and Augmentation Python – For preprocessing and combining real and synthetic images. OpenCV – For image manipulation and augmentation
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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.
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Deployment and Explainability Flask – To create a web-based interface for real-time model interaction.
