Cystoscopic Image Classification Based on Combining MLP and GA

Document Type: Research Paper

Authors

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

2 University of Guilan, Guilan, Iran

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

10.22075/ijnaa.2020.4232

Abstract

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 blad-
der 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 con-
vergence, 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.

Keywords