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Urinary bladder tumor grade diagnosis using on-line trained neural networks

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  • D. K. Tasoulis
  • P. Spyridonos
  • N. G. Pavlidis
  • D. Cavouras
  • P. Ravazoula
  • G. Nikiforidis
  • M. N. Vrahatis
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Publication date1/12/2003
Host publicationKnowledge-Based Intelligent Information and Engineering Systems: 7th INternational Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part I
PublisherSpringer
Pages199-206
Number of pages8
Volume2773 PART 1
ISBN (Electronic)9783540452249
ISBN (Print)9783540408031
<mark>Original language</mark>English
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: 3/09/20035/09/2003

Conference

Conference7th International Conference, KES 2003
Country/TerritoryUnited Kingdom
CityOxford
Period3/09/035/09/03

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
PublisherSpringer Verlag
ISSN (Print)0302-9743

Conference

Conference7th International Conference, KES 2003
Country/TerritoryUnited Kingdom
CityOxford
Period3/09/035/09/03

Abstract

This paper extends the line of research that considers the application of Artificial Neural Networks (ANNs) as an automated system, for the assignment of tumors grade. One hundred twenty nine cases were classified according to the WHO grading system by experienced pathologists in three classes: Grade I, Grade II and Grade III. 36 morphological and textural, cell nuclei features represented each case. These features were used as an input to the ANN classifier, which was trained using a novel stochastic training algorithm, namely, the Adaptive Stochastic On-Line method. The resulting automated classification system achieved classification accuracy of 90%, 94.9% and 97.3% for tumors of Grade I, II and III respectively.