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

  • Authors:

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

  • Affiliations:

  • (Correspd. Tel.: +30 61 997745/ Fax: +30 61 997882/ E-mail: Spyridonos@med.upatras.gr) 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;Computer Laboratory, School of Medicine, University of Patras, Rio, Patras 265 00, Greece

  • Venue:
  • Journal of Computational Methods in Sciences and Engineering
  • Year:
  • 2006

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Abstract

Purpose: In this paper we address the demanding diagnostic problem of classifying tumors according to the degree of their malignancy by investigating the efficiency of Support Vector Machines (SVMs) and Probabilistic neural networks (PNN). Material and methods: 129 cases of urinary bladder carcinomas were diagnosed as high or low-risk according to the WHO grading system. Each case was represented by 36 automatically extracted nuclear features. Two different classification designs based on SVMs and PNNs were tested according to their ability in differentiating superficial urinary bladder carcinomas according to the degree of malignancy. Best feature combination for each classification scheme was obtained performing an exhaustive search in feature space and employing the leave-one-out method. Results: Both classification models (SVM and PNN) 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. Conclusion: The good performance and consistency of the SVM and PNN models render these techniques viable alternatives in the diagnostic process of assigning urinary bladder tumors grade.