Cystoscopic Image Classification Based on Combining MLP and GA

Document Type : Research Paper


1 Faculty of Computer Engineering, Shahroud University of Technology, Semnan, Iran

2 Faculty of Engineering, East Guilan, University of Guilan, Guilan, Iran

3 Faculty of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands


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, 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. Another challenge in neural networks is determining the initial weights. In cystoscopy images, randomized initial weights should not be used due to a small number of images collected. Therefore, the genetic algorithm (GA) is applied to determine the initial weight. The proposed method was evaluated on 540 bladder cystoscopy images in three classes of blood in urine, benign and malignant masses. Based on the simulated results, the proposed method achieved a 7% decrease in error and increased the convergence speed of the proposed method in the classification of cystoscopy images, compared to the other competing methods.


Volume 11, Issue 1
April 2020
Pages 93-105
  • Receive Date: 03 February 2019
  • Revise Date: 12 August 2019
  • Accept Date: 05 September 2019