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Neural Network Design for the Recurrence Prediction of Post-Operative Non-Metastatic Kidney Cancer Patients

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Neural Network Design for the Recurrence Prediction of Post-Operative Non-Metastatic Kidney Cancer Patients.pdf (155.1Kb)
Date
2016
Author
Tander, Baran
Özmen, Atilla
Özden, Ender
Abstract
In this paper various post-operative recurrence estimation models called nomograms for the kidney cancer patients without any metastates are introduced and novel systems based on a Multilayer Perceptron Neural Network are designed to simplify and integrate the mentioned techniques which is believed to ease the physician' s post-operative follow up procedures. The parameters effecting the recurrence are the TNM stage tumor size and nuclear (Fuhrman) grade the existance of necrosis and vascular invasion. Independent systems for two of the individual prediction methods as well as a system that combines these are designed and performance analyses are carried out to verify the reliability. © 2015 Chamber of Electrical Engineers of Turkey.

Source

2015 9th International Conference on Electrical and Electronics Engineering (ELECO)

Pages

162-165

URI

https://hdl.handle.net/20.500.12469/1296
https://doi.org/10.1109/ELECO.2015.7394627

Collections

  • Araştırma Çıktıları / Scopus [1345]
  • Elektrik-Elektronik Mühendisliği / Electrical - Electronics Engineering [330]

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