  Ureteroscopy and cystoscopy are the gold standard methods to identify and
treat tumors along the urinary tract. It has been reported that during a normal
procedure a rate of 10-20 % of the lesions could be missed. In this work we
study the implementation of 3 different Convolutional Neural Networks (CNNs),
using a 2-steps training strategy, to classify images from the urinary tract
with and without lesions. A total of 6,101 images from ureteroscopy and
cystoscopy procedures were collected. The CNNs were trained and tested using
transfer learning in a two-steps fashion on 3 datasets. The datasets used were:
1) only ureteroscopy images, 2) only cystoscopy images and 3) the combination
of both of them. For cystoscopy data, VGG performed better obtaining an Area
Under the ROC Curve (AUC) value of 0.846. In the cases of ureteroscopy and the
combination of both datasets, ResNet50 achieved the best results with AUC
values of 0.987 and 0.940. The use of a training dataset that comprehends both
domains results in general better performances, but performing a second stage
of transfer learning achieves comparable ones. There is no single model which
performs better in all scenarios, but ResNet50 is the network that achieves the
best performances in most of them. The obtained results open the opportunity
for further investigation with a view for improving lesion detection in
endoscopic images of the urinary system.
