Exploring texture Transfer Learning for Colonic Polyp Classification via Convolutional Neural Networks

This work addresses Transfer Learning via Convolutional Neural Networks (CNN's) for the automated classification of colonic polyps in eight HD-endoscopic image databases acquired using different modalities. For this purpose, we explore if the architecture, the training approach, the number of classes, the number of images as well as the nature of the images in the training phase can influence the results. The experiments show that when the number of classes and the nature of the images are similar to the target database, the results are improved. Also, the better results obtained by the transfer learning compared to the most used features in the literature suggest that features learned by CNN's can be highly relevant for automated classification of colonic polyps.