An improved fuzzy-differential evolution approach applied to classification of tumors in liver CT scan images

  • Banafsheh AmirHosseini
  • Rahil HosseiniEmail author
Original Article


Fuzzy inference systems have been frequently used in medical diagnosis for managing uncertainty sources in the medical images. In addition, fuzzy systems have high level of interpretability because of using linguistic terms for knowledge representation through the reasoning process. The evolutionary algorithms can be applied for optimization of the systems. This article takes advantages of differential evolutionary algorithm to search the problem space more intelligently using the knowledge of distance vector of the candidate solutions. For this, a hybrid fuzzy-DE model has been proposed for the problem of classification of the Haptic metastasis tumors through information obtained from the features measured in the CT scan images by radiologists. The hybrid fuzzy-DE model was evaluated using a real liver cancer dataset obtained from the Noor medical imaging center in Tehran. The results of the hybrid proposed models were compared with the diagnosis of the specialists, the results reveal that the proposed fuzzy-DE model has high capability for diagnosis of the hepatic metastasis tumors with an accuracy of 99.24% with 95% confidence interval (98.32 100) in terms of area under the ROC curve. The proposed model outperforms the multilayer perceptron (MLP) neural network and fuzzy-genetic algorithm (GA). The proposed model improves the trade-offs between the accuracy and interpretability by providing a model with high accuracy using less input variables. The hybrid fuzzy-DE model is promising to assist the medical specialists for early diagnosis of liver cancer and save more people lives.


Fuzzy expert system Differential evolution Classification of tumors Liver cancer 



  1. 1.
    Ebrahimi Daryani N, Saberi H, Pashayi M, Taaher M, Shirzad S (2010) Hepatic tumors and the way it is diagnosed and faced, digestion. 3:209–226Google Scholar
  2. 2.
    Hosseini R, Mazinani M Classification of the sources of uncertainty in medical image processing and analysis intelligent systems, In Proc. of 9th SASTECH Conference, Mashhad, Iran, December 2014Google Scholar
  3. 3.
    Khezri R, Hosseini R, Mazinani M (2014) A fuzzy rule-based expert system for the prognosis of the risk of development of the breast cancer. Int J Eng Trans A 27:1557–1564Google Scholar
  4. 4.
    Fazel Zarandi MH, Zarinbal M, Izadi M (2011) Systematic image processing for diagnosing brain tumors: a type-II fuzzy expert system approach. Appl Soft Comput 11:285–294CrossRefGoogle Scholar
  5. 5.
    Hosseini R, Qanadli SD, Barman S, Mazinani M, Ellis T, Dehmeshki J (2012) An automatic approach for learning and tuning Gaussian interval type-2 fuzzy membership functions applied to lung CAD classification system. IEEE Trans Fuzzy Syst 20:224–234CrossRefGoogle Scholar
  6. 6.
    Hosseini R, Akhoondi R (2016) A fuzzy expert system for prognosis of the risk of development of heart disease. J Adv Comput Res 7:101–114Google Scholar
  7. 7.
    AmirHosseini B, Hosseini R, Mazinani M (2017) A hybrid fuzzy-GA approach applied to an expert system for diagnosis of liver tumor. J Soft Comput Inform 5:45–52Google Scholar
  8. 8.
    Amirhosseini B, Hosseeini R, Mzinani M (2016) An MLP neural network and fuzzy inferecence system for diagnosis of metastasis in liver, first international conference on new research achievements in electrical and computer engineering, 1–10Google Scholar
  9. 9.
    Si T, Hazra S, Jana ND (2012) Artificial neural network training using differential evolutionary algorithm for classification. Adv Intell Soft Comput 132:769–778CrossRefGoogle Scholar
  10. 10.
    Dash T, Kumar Nayak S, Behera HS (2014) Hybrid gravitational search and particle swarm based fuzzy MLP for medical data classification. Comput Intell Data Min 1:35–43Google Scholar
  11. 11.
    Gunasundari S, Janakiraman S (2014) A hybrid PSO-SFS-SBS algorithm in feature selection for liver cancer data. Power Electron Renew Energy Syst 326:1369–1376CrossRefGoogle Scholar
  12. 12.
    Koyuncu H Ceylan R (2015) Scout particle swarm optimization, 6 th European Conference of the International Federation for Medical and Biological Engineering, TurkeyGoogle Scholar
  13. 13.
    Lin SW, Chen SC, Wu WJ, Chen CH (2009) Parameter determination and feature selection for back-propagation network by particle swarm optimization. Int J Knowl Inf Syst 21:249–266CrossRefGoogle Scholar
  14. 14.
    Jiang H, Tang F Zhang X (2010) Liver cancer identification based on PSO-SVM model, 11th International Conference on Control Automation Robotics & Vision (ICARCV), SingaporeGoogle Scholar
  15. 15.
    Gunasundari S, Janakiraman S (2013) Improved feature selection based on particle swarm optimization for liver disease diagnosis. Springer International Publishing 8298:214–225Google Scholar
  16. 16.
    Jiang H, Zou L (2013) A hybrid PSO-SA optimizing approach for SVM model in classification. Int J Biomath 6(5):1350036-1–1350036-18CrossRefGoogle Scholar
  17. 17.
    Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30:1145–1159CrossRefGoogle Scholar
  18. 18.
    Brownlee J (2011) Clever algorithms nature-inspired programming recipes 1st edition, LuluGoogle Scholar
  19. 19.
    Bu Ishaq M, Eftekhari M (2014) A new memetic approach in numerical optimization algorithm: hierarchy differential evolution, the 9th symposium on advances in science and technology (9thSASTech), Mashhad, IranGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Shahr-e-Qods BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer Engineering, Shahr-e-Qods BranchIslamic Azad UniversityTehranIran

Personalised recommendations