The Application Of Fuzzy Neural Network To Lung Cancer Diagnosis

Lung cancer is a common malignant tumor in the world today, which has become the main reason of cancer patients' death. In China, the incidence of lung cancer has risen from the sixth to the first, and the mortality is in the top of urban population malignant tumor. In rural areas, the increase is significantly larger than that in urban, especially the trend of the rural women's mortality rise is more prominent. Because there are no or few specific symptoms in the early period of lung cancer, it is difficult to be detected. It has usually metastasized when it is detected. Early diagnosis has an important prognostic value and has a huge impact on treatment planning. So the early diagnosis and treatment is a necessary method to improve the survival rate and reduce the mortality of the patients with lung cancer.The recent progress of lung cancer diagnosis was reviewed in the paper. Fuzzy neural network was introduced and discussed. Fuzzy neural network was the combination of artificial neural network and fuzzy theory. Artificial neural network is a computational model based on the brain; it is a powerful tool for data classification and pattern identification because it has good adaptability, self-organization and self-learning ability. The prominent feature of fuzzy logic is to describe logical meaning to more naturally and directly that human are accustomed, which is suitable to express direct or high level of knowledge. And fuzzy neural network was used to diagnose lung cancer, developing a new approach for lung cancer diagnosis. The network is based on 21 features extracted from chest CT and 5 clinical parameters. The aim of the study was to improve the lung cancer diagnosis accuracy.Methods:The usefulness of a fuzzy neural network with Gaussian membership function for distinguishing between lung cancer and benign cases was studied to improve lung cancer diagnosis.13 non-binary parameters of 26 characteristic parameters were fuzzed with Gaussian membership function. Every input variable was divided into 3 fuzzy subspaces, using large(L), medium(M) and small(S) 3 linguistic variables to express. Each input variable had 3 fuzzy neurons, corresponding to subject function values in 3 fuzzy subspaces. Output after fuzziness was 39 elements. Then, the fuzzed outputs added with the other 13 binary parameters were used as inputs of the BP neural network.117 cases, including lung cancer and benign cases, were divided into training set and test set randomly. And these cases were used to train fuzzy neural network and select appropriate hidden nodes. The test set was also used to test the performance of the trained fuzzy neural network in differentiation of benign from malignant pulmonary nodules. The performances of Gaussian membership function fuzzy neural network were compared with that of fuzzy neural network with triangle membership function.Conclusions:The performance of the fuzzy neural network with Gaussian membership function was better than that of the fuzzy neural network with triangle membership function in prediction of probability of malignancy in pulmonary nodules. There were 2 false-positive and 2 false-negative in Gaussian membership function fuzzy neural network. But in triangle membership function fuzzy neural network, there was 1 false-negative more than that of in GMF FNN. The diagnostic accuracy rate of fuzzy neural network with Gaussian membership function was 91%, which was 3 percentage points higher than that of fuzzy neural network with triangular membership function. And the sensitivity of Gaussian membership function fuzzy neural network was better than that of triangle membership function fuzzy neural network. So, the fuzzy neural network with Gaussian membership function has the potential to improve the diagnostic accuracy of distinction between the benign and malignant cases.