Effective Feature Selection for Pre-Cancerous Cervix Lesions Using Artificial Neural Networks

International Journal of Smart Electrical Engineering

مقاله 7، دوره 01، شماره 03، آذر 2012، صفحه 199-204   

نویسندگان

Farnaz Rouhbakhsh؛ Fardad Farokhi؛ Kaveh Kangarloo

Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,

چکیده

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

کلیدواژه‌ها

Image classification؛ Artificial Neural Network؛ Feature Selection؛ Colposcopic images

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