effective feature selection for pre-cancerous cervix lesions using artificial neural networks

since most common form of cervical cancer starts with pre-cancerous changes, a flawless detection of these changes becomes an important issue to prevent and treat the cervix cancer. there are 2 ways to stop this disease from developing. one way is to find and treat pre-cancers before they become true cancers, and the other is to prevent the pre-cancers in the first place. the presented approach uses precancerous images which are taken from a digital colposcope, and a set of texture and color features is extracted which includes low and high grade sil (squamous interepithelial lesion ) .after extracting, features are fed to a classifier, which could be knn,rbf,mlp and neuro-fuzzy network and after training effective features are selected using uta algorithm for each classifier individually. finally, results come in a comparison table, show that the landa fourteenth, theta-x and together with neuro-fuzzy classifier have the best overall performance. this approach has an acceptable and simple early diagnosis of cervix cancer and may have found clinical application