Applications Of Neural Network Technology In The Diagnosis Of Ovarian Tumors

At present, digital images processing, pattern recognition and artificial interlligence are widely applied in the fields of biology and medicine. Some achievements have been gained by these techniques. But there is few reports on the pathologic diagnosis system which can be used to recognize cells images in the mean of neural network.In the paper,based in the detailed investigation of ovarian cells from ascites images recognition and feature extraction,results of utilizing neural network to diagnose and classify cells images are satisfiable and valuable in clinical application.In the paper, the original ovarian cells from ascites are preprocessed and samples are gained.Features parameters of morphology are extracted from images of cells samples.The images of cells samples are recognized and classified by Multilayer Perceptron Neural Network and Radial Basis Function Neural Network. Several arithmetics of MLPNN and RBFNN are discussed,and cross entropy arithmetic are suggested.Among the recognized results,the recognition rate and classification of RBFNN and MLPNN with BP arithmetic based on adaptive are the best one.In the paper, the characteristicsost number and the linear relationship between the classified effect are studied. Features, classification and recognition rate increase. But if there are linear relationship between the features ,redundant data reduce recognition rate.Conbined computer technology with practical experiences of pathology experts and based on processing techniques of medicine images,neural network can be used to recognize cells images and shows its significant value in clinical diagnosis and medical research at present and in the future.