A transfer-learning approach for lesion detection in endoscopic images from the urinary tract

TL;DR

This study evaluates the use of transfer learning with CNNs to improve lesion detection in urinary tract endoscopic images, achieving high accuracy and suggesting potential for clinical application.

Contribution

It introduces a two-step transfer learning approach with multiple CNN architectures for urinary lesion detection, comparing their effectiveness across different datasets.

Findings

ResNet50 achieved up to 0.987 AUC on ureteroscopy images.

VGG performed best on cystoscopy images with 0.846 AUC.

Combining datasets generally improved model performance.

Abstract

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.