Transfer Learning for Oral Cancer Detection using Microscopic Images
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Oral cancer has more than 83 however, only 29 techniques can detect patterns of oral cancer cells and can aid in its early detection. In this work, we present the first results of neural networks for oral cancer detection using microscopic images. We compare numerous state-of-the-art models via transfer learning approach and collect and release an augmented dataset of high-quality microscopic images of oral cancer. We present a comprehensive study of different models and report their performance on this type of data. Overall, we obtain a 10-15 transfer learning methods compared to a simple Convolutional Neural Network baseline. Ablation studies show the added benefit of data augmentation techniques with finetuning for this task.
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