Cystoscopic image classification by an ensemble of VGG-nets

Document Type : Research Paper


Faculty of Technology and Engineering (Eastern Guilan), University of Guilan, Guilan, Iran.


Over the last three decades, artificial intelligence has attracted lots of attentions in medical diagnosis tasks. However, few studies have been presented to assist urologists to diagnose bladder cancer in spite of its high prevalence worldwide. In this paper, a new computer aided diagnosis system is proposed to classify four types of cystoscopic images including malignant masses, benign masses, blood in urine, and normal. The proposed classifier is an ensemble of a well-known type of convolutional neural networks (CNNs) called VGG-Net. To combine the VGG-Nets, bootstrap aggregating approach is used. The proposed ensemble classifier was evaluated on a dataset of 720 images. Based on the experiments, the presented method achieved an accuracy of 63% which outperforms base VGG-Nets and other competing methods.


[1] Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, Greedy layer-wise training of deep networks,  Adv. Neural Inf. Process. Syst. 19 (2006).
[2] Y. Bengio, A. Courville and P. Vincent, Representation learning: A review and new perspectives, IEEE Trans. Pattern Anal. Machine Intell. 35(8) (2016) 1798-1828.
[3] O. Cicek, A. Abdulkadir, S.S. Lienkamp, T. Brox, and O. Ronneberger, 3D U-Net: Learning dense volumetric segmentation from sparse annotation, Int. Conf. Medical Image Comput. Computer-assisted Intervention, Springer, 2016.
[4] H. Greenspan, B. Van Ginneken and R.M. Summers, Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans. Medical Imag.  35(5) (2016) 1153-1159.
[5] J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, and T. Chen, Recent advances in convolutional neural networks, Pattern Recog. 77 (2018) 354-377.
[6] G.E. Hinton, S. Osindero and Y.W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18(7) (2006) 1527-1554.
[7] S. Hashemi, H. Hassanpour, E. Kozegar, and T. Tan, Cystoscopy image classification using deep convolutional neural networks, Int. J. Nonlinear Anal. Appl.10(1) (2019) 193-215.
[8] S. Hashemi, H. Hassanpour, E. Kozegar and T. Tan. Cystoscopic image classification based on combining MLP and GA, Int. J. Nonlinear Anal. Appl. 11(1) (2020) 93-105.
[9] K. Kamnitsas, C. Ledig, V.F.J. Newcombe, J.P. Simpson, A.D. Kane, D.K. Menon, D. Rueckert, and B. Glocker, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Anal. 36 (2017) 61-78.
[10] L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, John Wiley & Sons, Inc., 2004.
[11] F. Milletari, N. Navab, and S.A. Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation, Fourth Int. Conf. 3D Vision (3DV), IEEE, 2016.
[12] A. Prasoon, K. Petersen, C. Igel, F. Lauze, E. Dam, and M. Nielsen, Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, Int. Conf.  Medical Image Comput. Computer-assisted Intervention,  Springer, 2013.
[13] D. Ravı, Deep learning for health informatics, IEEE J.  Biomed. Health Inf. 21(1) (2016) 4-21.
[14] O. Ronneberger, P. Fischer and T. Brox. U-net: Convolutional networks for biomedical image segmentation, Int. Conf. Medical Image Comput. Computer-assisted Intervention, Springer, 2015.
[15] J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks 61 (2015) 85-117.
[16] A.A.A. Setio, F. Ciompi, G. Litjens, P Gerke, C. Jacobs, S.J. Van Riel, M.M.W. Wille, M. Naqibullah, C.I.  Sánchez, and B. Van Ginneken, Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks, IEEE Trans. Medical Imag. 35(5) (2016) 1160-1169.
[17] N. Simforoosh, Iranian Textbook of Urology, Shahid Beheshti University of Medical Sciences, Tehran, 2007.
[18] Y. Song, L. Zhang, S. Chen, D. Ni, B. Lei, and T. Wang, Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning, IEEE Trans. Biomed. Engin. 62(10) (2015) 2421-2433.
[19] C. Szegedy, W. Liu, Y. Jia, and P. Sermanet, Going deeper with convolutions, Proc.  IEEE Conf. Comput. Vision Pattern Recognition, 2015, pp. 1-9.
[20] W. Yang, Y. Chen, Y. Liu, L. Zhong, G. Qin, Z. Lu, Q. Feng, and W. Chen, Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain, Medical Image Anal. 35 (2017) 421-433.
Volume 12, Issue 1
May 2021
Pages 693-700
  • Receive Date: 21 October 2020
  • Revise Date: 03 February 2021
  • Accept Date: 15 February 2021