Computer-aided Diagnosis of Cervical Cytopathology Based on Neural Network Ensemble Model

Aiming to the automatic screening of cervical cytopathological images, an artificial intelligence based automatic diagnosis-assisted method was proposed. First of all, adaptive dual threshold method was used to detect the cervical cytopathological images initially. Secondly, improved Chan-Vase model was used to precisely extract different areas of adhesive cell cluster. After that, the related feature set was built according to the diagnostic rules of pathological experts. At last, neural network ensemble was applied to normal or suspected lesions two-classification recognition. The result of the experiment showed that cervical cell lesions could be effectively distinguished according to classification with this method, which had high accuracy (84%) and low rate of misjudgment (2.1%0), meeting the practical requirement of pathological diagnosis, which is reducing the miscalculating of the suspected lesions to normal ones, meanwhile assuring the diagnostic accuracy.