Comparative evaluation of support vector machines and probabilistic neural networks in superficial bladder cancer classification

  • Authors:

  • P. Spyridonos;P. Petalas;D. Glotsos;G. Nikiforidis;D. Cavouras;P. Ravazoula

  • Affiliations:

  • Computer Laboratory, School of Medicine, University of Patras, Rio, Patras, 265 00, Greece;Computer Laboratory, School of Medicine, University of Patras, Rio, Patras, 265 00, Greece;Computer Laboratory, School of Medicine, University of Patras, Rio, Patras, 265 00, Greece;Computer Laboratory, School of Medicine, University of Patras, Rio, Patras, 265 00, Greece;Department of Medical Instrumentation Technology, Technological Education Institution of Athens, Ag. Spyridonos Street, Aigaleo, 122 10, Athens, Greece;Department of Pathology, University Hospital, Rio, Patras, 265 00, Greece

  • Venue:
  • ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
  • Year:
  • 2003

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Abstract

In this study a comparative evaluation of Support Vector Machines (SVMs) and Probabilistic neural networks (PNNs) was performed exploring their ability to classify superficial bladder carcinomas as low or high-risk. Both classification models resulted in a relatively high overall accuracy of 85.3% and 83.7% respectively. Descriptors of nuclear size and chromatin cluster patterns were participated in both best feature vectors that optimized classification performance of the two classifiers. Concluding, the good performance and consistency of the SVM and PNN models enforces the belief that certain nuclear features carry significant diagnostic information and renders these techniques viable alternatives in the diagnostic process of assigning urinary bladder carcinomas grade.