  In this paper we report results for recognizing colorectal NBI endoscopic
images by using features extracted from convolutional neural network (CNN). In
this comparative study, we extract features from different layers from
different CNN models, and then train linear SVM classifiers. Experimental
results with 10-fold cross validations show that features from first few
convolution layers are enough to achieve similar performance (i.e., recognition
rate of 95%) with non-CNN local features such as Bag-of-Visual words, Fisher
vector, and VLAD.
