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

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.