Abstract
Accurate categorization of cirrhosis liver image in ultrasound modality is of great importance in medical diagnosis and treatment. Health informatics is important as it provides quick predictions on diseases based on nearness of symptoms. Modelling solutions for the same on cloud using deep learning is the motivation of this paper. Here, we propose a deep learning model associated with correlation based feature selection method for cirrhosis image classification. We compare the results with three other conventional classifiers algorithms to improve the better classification accuracy. First in pre-processing stage, noises are eliminated from pathological scan images by using modified laplacian pyramid non-linear diffusion filter. From the pre-processed scan images, each cirrhosis region is obtained under the guidance of radiology or physicians. Then, after extracting the complete features of each patch by gray level, local binary pattern and scale invariant feature, a feature selection technique is applied to choice the predominant texture features for each classifier. Finally a convolution neural network is implemented to improve the performance of classifiers in terms of sensitivity, specificity and accuracy. Convolution neural network algorithm with two hidden layers gives more accuracy in classifying cirrhosis image with 98% sensitivity. Experiments are carried out with 990 cirrhosis image patches which demonstrates that our proposed deep learning classifier perform 100% well than original classifiers in terms of accuracy.
Similar content being viewed by others
References
Zakeri, F.S., Behnam, H., Ahmadinejad, N.: Classification of benign and malignant breast masses based on shape and texture features in sonography images. J. Med. Syst. 36(3), 1621–1627 (2012)
Ding, J., Cheng, H.D., Huang, J., Zhang, Y.: Multiple-instance learning with global and local features for thyroid ultrasound image classification in 7th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE Xplore (2014)
Owjimehr, M., Danyali, H., Helfroush, M.S.: Fully automatic segmentation and classification of liver ultrasound images using completed LBP texture features. 22nd Iranian Conference on Electrical Engineering (ICEE), IEEE Xplore (2014)
Schnorrenberg, F., Pattichis, C.S., Schizas, C.N.: Computer-aided classification of breast cancer nuclei. J. Technol. Health Care 4(2), 147–161 (1996)
Xia, B., Jiang, H., Liu, H.: A Novel Hepatocellular carcinoma image classification method based on voting ranking random forests. J. Comput. Math. Methods Med. (2016)
Kontoravdis, D., Likas, A., Krakitsos, P.: Cytological diagnosis based on fuzzy neural networks. J. Intell. Syst. 8(1–2), 55–80 (1998)
Irshad, H.: Automated mitosis detection in histopathology using morphological and multi-channel statistics features. J. Pathol. Inf. 4(1), 10 (2013)
Mat-Isa, N.A., Mashor, M.Y., Othman, N.H.: An automated cervical pre-cancerous diagnostic system. Artif. Intell. Med. 42(1), 1–11 (2008)
Stoklasa, R., Majtner, T., Svoboda, D.: Efficient k-NN based HEp-2 cells classifier. J. Pattern Recognit. 47(7), 2409–2418 (2014)
Hwang, K.H., Lee, H., Choi, D.: Medical image retrieval: Past and present. J. Healthcare Res. Inf. 18(1), 3–9 (2012)
Praveena, A., Smys, S.: Prevention of inference attacks for private information in social networking sites in Inventive Systems and Control (ICISC). In: International Conference on 2017 Jan 19, pp. 1–7. IEEE Xplore (2017)
Praveena, A., Smys, S.: Anonymization in social networks: a survey on the issues of data privacy in social network sites. Int. J. Eng. Comput. Sci. 5(3), 15912–8 (2016)
Smys, S., Kumar, A.D.: Secured WBANs for pervasive m-healthcare social networks in Intelligent Systems and Control (ISCO), 2016 10th International Conference on 2016 Jan 7, pp. 1–4. IEEE Xplore (2016)
Wagner, R., Smith, S., Sandrik, J., Lopez, H.: Statistics of speckle in ultrasound B-scans. IEEE Trans. Sonics. Ultrason. 30(3), 156–163 (1983)
Burt, P.J., Adelson, E.A.: The laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intll. 12(7), 629–639 (1990)
Zhang, F., Yoo, Y.M., Koh, L.M., Kim, Y.: Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction. IEEE Trans. Med. Imaging 26(2), 200–211 (2007)
Suganya, R., Rajaram, S., Deebika, G.: An efficient method for speckle reduction in ultrasound liver images for e-health applications. Distrib. Comput. Internet Technol. LNCS 8337, 311–321 (2014)
Lee, W.L., Chen, Y.C., Hsieh, K.S.: Ultrasonic liver tissue classification by fractal feature vector based on M-band wavelet transform. IEEE Trans. Med. Imaging 22(3), 382–392 (2003)
Haralick, R.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)
Hall, M.A., Smith, L.A.: Practical feature subset selection for machine learning. In: Proceedings of the 21st Australian Computer Science Conference, pp. 181–191 (1998)
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sanchez, C.I.: A survey on deep learning in medical image analysis. J. Med. Image Anal. (2017)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Suganya, R., Rajaram, S. An efficient categorization of liver cirrhosis using convolution neural networks for health informatics. Cluster Comput 22 (Suppl 1), 47–56 (2019). https://doi.org/10.1007/s10586-017-1629-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1629-2