Abnormality classification in small datasets of capsule endoscopy images
Filipe Rhodes da Fonseca, Beatriz Nunes, Marta Salgado, A. Cunha
Abstract
Capsule endoscopy made it possible to observe the inner lumen of the small bowel, but with the cost of a longer duration to process its resulting videos. Therefore, the scientific community has developed several machine learning strategies to help in detecting abnormalities in these videos. The published algorithms are typically trained and evaluated on small sets of images, ultimately not proving to be efficient when applied to full videos. In this experiment, we explored the problem of abnormality classification within an unbalanced dataset of images extracted from video capsule endoscopies, based on a vector feature extracted from the deepest layer of pre-trained Convolution Neural Networks to evaluate the impact of transfer learning with a small number of samples. The results showed that there is a reliable model on the classification task using small portions of data from video capsule endoscopies.