Cystoscopy image classification using deep convolutional neural networks

In the past three decades, the use of smart methods in medical diagnostic systems has attracted the attention of many researchers. However, no smart activity has been provided in the field of medical image processing for diagnosis of bladder cancer through cystoscopy images despite the high prevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) and a multilayer neural network was applied to classify bladder cystoscopy images. One of the most important issues in training phase of neural networks is determining the learning rate because selecting too small or large learning rate leads to slow convergence, volatility and divergence, respectively. Therefore, an algorithm is required to dynamically change the convergence rate. In this respect, an adaptive method was presented for determining the learning rate so that the multilayer neural network could be improved. In this method, the learning rate is determined using a coefficient based on the difference between the accuracy of training and validation according to the output error. In addition, the rate of changes is updated according to the level of weight changes and output error. The proposed method was evaluated on 720 bladder cystoscopy images in four classes of blood in urine, benign and malignant masses. Based on the simulated results, the second proposed method (CNNs) achieved at least 17% decrease in error and increased the convergence speed of the proposed method in the classification of cystoscopy images, compared to the other competing methods.