Cystoscopic Image Classification Based on Combining MLP and GA

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.

[1]  T. Jeyaselvi,et al.  Support Vector Machine Classification For MRI Images , 2012 .

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  T. Williamson,et al.  Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy , 2015, Comput. Medical Imaging Graph..

[4]  Zakaria Suliman Zubi,et al.  Improves Treatment Programs of Lung Cancer Using Data Mining Techniques , 2014 .

[5]  Rosziati Ibrahim,et al.  A Framework for Medical Images Classification Using Soft Set , 2013 .

[6]  R. J. Ramteke,et al.  Automatic Medical Image Classification and Abnormality Detection Using K-Nearest Neighbour , 2012 .

[7]  Linda Bushnell,et al.  Fast Modifications of the SpikeProp Algorithm , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[8]  Divya Tomar,et al.  A survey on Data Mining approaches for Healthcare , 2013, BSBT 2013.

[9]  Martin T. Hagan,et al.  Neural network design , 1995 .

[10]  Mohd Nasir Taib,et al.  Comparison between KNN and ANN Classification in Brain Balancing Application via Spectrogram Image , 2012 .

[11]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[12]  Usman Qamar,et al.  Artificial Neural Network based Classification of Lungs Nodule using Hybrid Features from Computerized , 2015 .

[13]  B. Schrauwen,et al.  Extending SpikeProp , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[14]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[15]  Chao Li,et al.  Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer , 2012, Comput. Math. Methods Medicine.

[16]  J S Liaw,et al.  Dynamic synapse: A new concept of neural representation and computation , 1996, Hippocampus.

[17]  Payman Moallem,et al.  Spiking neural network learning algorithms: Using learning rates adaptation of gradient and momentum steps , 2010, 2010 5th International Symposium on Telecommunications.

[18]  Hamid Hassanpour,et al.  Cystoscopy Image Classification Using Deep Convolutional Neural Networks , 2019 .

[19]  Walaa Hussein Ibrahim,et al.  MRI brain image classification using neural networks , 2013, 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).

[20]  Vladimir B. Bajic,et al.  An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications , 2011, Intell. Autom. Soft Comput..

[21]  Ganesh Madhikar,et al.  Survey on Brain Tumour Detection and Classification Using Image Processing , 2018 .

[22]  Robert B. Fisher,et al.  A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions , 2013 .

[23]  Wenyu Yang,et al.  A remark on the error-backpropagation learning algorithm for spiking neural networks , 2012, Appl. Math. Lett..

[24]  A. Iyyanarappan,et al.  Glaucomatous Image Classification Using Wavelet Based Energy Features And PNN , 2014 .

[26]  M. Madheswaran,et al.  Classification of brain MRI images using support vector machine withvarious Kernels. , 2015 .

[27]  H. Markram,et al.  Potential for multiple mechanisms, phenomena and algorithms for synaptic plasticity at single synapses , 1998, Neuropharmacology.

[28]  Jianguo Xin,et al.  Supervised learning with spiking neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[29]  M. Madheswaran,et al.  Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm , 2010, ArXiv.

[30]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[31]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[32]  Theodore W. Berger,et al.  A new dynamic synapse neural network for speech recognition , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[33]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.