Abstract
In this paper a new technique for classification of patients affected by Crohn’s disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The effectiveness of the proposed technique has been proved using a real human patient dataset collected at the University of Palermo Policlinico Hospital (UPPH dataset) and composed of 300 patients. The KSVM classification results have been compared against the histological specimen results, which are the adopted Ground-Truth for CD diagnosis. The achieved results (Sensitivity: 94,80%; Specificity: 100,00%; Negative Predictive Value: 95,06%; Precision: 100,00%; Accuracy: 97,40%; Error: 2,60%) show that the proposed technique results are comparable or even better than manual reference methods reported in literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Maglinte, D.D., Gourtsoyiannis, N., Rex, D., Howard, T.J., Kelvin, F.M.: Classification of small bowel Crohn’s subtypes based on multimodality imaging. Radiol. Clin. North Am. 41(2), 285–303 (2003)
Bhatnagar, G., Stempel, C., Halligan, S., Taylor, S.A.: Utility of MR enterography and ultrasound for the investigation of small bowel CD. J. Magn. Reson. Imaging 45, 1573–1588 (2016)
Lo Re, G., Midiri, M.: Crohn’s Disease: Radiological Features and Clinical-Surgical Correlations. Springer, Heidelberg (2016)
Gomollón, F., Dignass, A., Annese, V., Tilg, H., Van Assche, G., Lindsay, J.O., Peyrin-Biroulet, L., Cullen, G.J., Daperno, M., Kucharzik, T., et al.: 3rd European evidence-based consensus on the diagnosis and management of Crohn’s disease 2016: part 1: diagnosis and medical management. J. Crohns Colitis 11, 3–25 (2016). jjw168
Peloquin, J.M., Pardi, D.S., Sandborn, W.J., Fletcher, J.G., McCollough, C.H., Schueler, B.A., Kofler, J.A., Enders, F.T., Achenbach, S.J., Loftus, E.V.: Diagnostic ionizing radiation exposure in a population-based cohort of patients with inflammatory bowel disease. Am. J. Gastroenterol. 103(8), 2015–2022 (2008)
Sinha, R., Verma, R., Verma, S., Rajesh, A.: Mr enterography of Crohn disease: part 1, rationale, technique, and pitfalls. Am. J. Roentgenol. 197(1), 76–79 (2011)
Panes, J., Bouzas, R., Chaparro, M., García-Sánchez, V., Gisbert, J., Martínez de Guereñu, B., Mendoza, J.L., Paredes, J.M., Quiroga, S., Ripollés, T., et al.: Systematic review: the use of ultrasonography, computed tomography and magnetic resonance imaging for the diagnosis, assessment of activity and abdominal complications of Crohn’s disease. Aliment. Pharmacol. Ther. 34(2), 125–145 (2011)
Steward, M.J., Punwani, S., Proctor, I., Adjei-Gyamfi, Y., Chatterjee, F., Bloom, S., Novelli, M., Halligan, S., Rodriguez-Justo, M., Taylor, S.A.: Non-perforating small bowel CD assessed by MRI enterography: derivation and histopathological validation of an MR-based activity index. Eur. J. Radiol. 81(9), 2080–2088 (2012)
Lo Re, G., Cappello, M., Tudisca, C., Galia, M., Randazzo, C., Craxì, A., Camma, C., Giovagnoni, A., Midiri, M.: CT enterography as a powerful tool for the evaluation of inflammatory activity in Crohn’s disease: relationship of CT findings with CDAI and acute-phase reactants. Radiol. Med. (Torino) 119(9), 658–666 (2014)
Tolan, D.J., Greenhalgh, R., Zealley, I.A., Halligan, S., Taylor, S.A.: Mr enterographic manifestations of small bowel Crohn disease 1. Radiographics 30(2), 367–384 (2010)
Sinha, R., Verma, R., Verma, S., Rajesh, A.: Mr enterography of Crohn disease: part 2, imaging and pathologic findings. Am. J. Roentgenol. 197(1), 80–85 (2011)
Chaplot, S., Patnaik, L., Jagannathan, N.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process. Control 1(1), 86–92 (2006)
Cocosco, C.A., Zijdenbos, A.P., Evans, A.C.: A fully automatic and robust brain MRI tissue classification method. Med. Image Anal. 7(4), 513–527 (2003)
Agnello, L., Comelli, A., Ardizzone, E., Vitabile, S.: Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis. Int. J. Imaging Syst. Technol. 26(2), 136–150 (2016)
Zhang, Y., Wu, L.: Weights optimization of neural network via improved BCO approach. Prog. Electromagnet. Res. 83, 185–198 (2008)
Comelli, A., Agnello, L., Vitabile, S.: An ontology-based retrieval system for mammographic reports. In: 2015 IEEE Symposium on Computers and Communication (ISCC), pp. 1001–1006. IEEE (2015)
Yeh, J.Y., Fu, J.: A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI. Expert Syst. Appl. 34(2), 1285–1295 (2008)
Patil, N., Shelokar, P., Jayaraman, V., Kulkarni, B.: Regression models using pattern search assisted least square support vector machines. Chem. Eng. Res. Des. 83(8), 1030–1037 (2005)
Wang, F.F., Zhang, Y.R.: The support vector machine for dielectric target detection through a wall. Prog. Electromagnet. Res. Lett. 23, 119–128 (2011)
Xu, Y., Guo, Y., Xia, L., Wu, Y.: An support vector regression based nonlinear modeling method for SiC MESFET. Prog. Electromagnet. Res. Lett. 2, 103–114 (2008)
Li, D., Yang, W., Wang, S.: Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine. Comput. Electron. Agric. 74(2), 274–279 (2010)
Son, Y.J., Kim, H.G., Kim, E.H., Choi, S., Lee, S.K.: Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc. Inform. Res. 16(4), 253–259 (2010)
Zhang, Y., Wang, S., Ji, G., Dong, Z.: An MR brain images classifier system via particle swarm optimization and Kernel support vector machine. Sci. World J. 2013, 9 (2013)
Tagluk, M.E., Akin, M., Sezgin, N.: Classıfıcation of sleep apnea by using wavelet transform and artificial neural networks. Expert Syst. Appl. 37(2), 1600–1607 (2010)
Agnello, L., Comelli, A., Vitabile, S.: Feature dimensionality reduction for mammographic report classification. Springer (2016)
Martiskainen, P., Järvinen, M., Skön, J.P., Tiirikainen, J., Kolehmainen, M., Mononen, J.: Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl. Anim. Behav. Sci. 119(1), 32–38 (2009)
Deris, A.M., Zain, A.M., Sallehuddin, R.: Overview of support vector machine in modeling machining performances. Procedia Eng. 24, 308–312 (2011)
Bermejo, S., Monegal, B., Cabestany, J.: Fish age categorization from otolith images using multi-class support vector machines. Fish. Res. 84(2), 247–253 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Comelli, A. et al. (2018). A Kernel Support Vector Machine Based Technique for Crohn’s Disease Classification in Human Patients. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-61566-0_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61565-3
Online ISBN: 978-3-319-61566-0
eBook Packages: EngineeringEngineering (R0)