a hybrid fuzzy-ga approach applied to an expert system for diagnosis of liver tumor

Applications of soft computing techniques have been concentrated for management of the uncertainty associated to the medical diagnosis in the recent decade. this article presents a fuzzy expert system (fes) for diagnose of metastasis in the liver which is one of the most common malignant hepatic tumors. furthermore, the proposed fes has been optimized using a genetic algorithm. the purpose of the hybrid fuzzyga approach is to adjust the fes parameters to enhance the system accuracy while the system interpretability has been kept. to experiment the proficiency of the intelligent fuzzyga model, the performance of the system was evaluated with real dataset of patients gathered from the noor medical imaging center in tehran. the fes performance was compared to the diagnosis of specialists before and after optimization with the ga. the results demonstrate that this system has a high capability in the management of uncertainty in the diagnosis with a high accuracy. the hybrid fuzzyga approach for hepatic tumors diagnosis is promising to assistant the specialists for early diagnosis of the cancer and saving more lives.