Brain tumor characterization using the soft computing technique of fuzzy cognitive maps

  • Authors:

  • E. I. Papageorgiou;P. P. Spyridonos;D. Th. Glotsos;C. D. Stylios;P. Ravazoula;G. N. Nikiforidis;P. P. Groumpos

  • Affiliations:

  • Department of Electrical and Computer Engineering, Laboratory for Automation and Robotic, University of Patras, Rion 26500, Greece;Computer Laboratory, School of Medicine, University of Patras, Rion 26500, Greece;Computer Laboratory, School of Medicine, University of Patras, Rion 26500, Greece;Department of Informatics and Telecommunications Technology TEI of Epirus, 47100 Kostakioi, Artas, Epirus, Greece;Department of Pathology, University Hospital of Patras, Rion 26500, Greece;Computer Laboratory, School of Medicine, University of Patras, Rion 26500, Greece;Department of Electrical and Computer Engineering, Laboratory for Automation and Robotic, University of Patras, Rion 26500, Greece

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

The characterization and accurate determination of brain tumor grade is very important because it influences and specifies patient's treatment planning and eventually his life. A new method for characterizing brain tumors is presented in this research work, which models the human thinking approach and the classification results are compared with other computational intelligent techniques proving the efficiency of the proposed methodology. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps (FCMs) to represent and model experts' knowledge (experience, expertise, heuristic). The FCM grading model classification ability was enhanced introducing a computational intelligent training technique, the Activation Hebbian Algorithm. The proposed method was validated for clinical material, comprising of 100 cases. FCM grading model achieved a diagnostic output of accuracy of 90.26% (37/41) and 93.22% (55/59) for brain tumors of low-grade and high-grade, respectively. The results of the proposed grading model present reasonably high accuracy, and are comparable with existing algorithms, such as decision trees and fuzzy decision trees which were tested at the same type of initial data. The main advantage of the proposed FCM grading model is the sufficient interpretability and transparency in decision process, which make it a convenient consulting tool in characterizing tumor aggressiveness for every day clinical practice.