Transfer Learning for Oral Cancer Detection using Microscopic Images
This work aims to improve the early detection of oral cancer, which could significantly increase survival rates for patients.
The paper addresses the low early detection rate of oral cancer by applying deep learning to microscopic images. They achieved a 10-15% absolute improvement over a CNN baseline using transfer learning methods.
Oral cancer has more than 83% survival rate if detected in its early stages, however, only 29% of cases are currently detected early. Deep learning 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% absolute improvement with 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.