Cervical Cancer Diagnostic System Using Adaptive Fuzzy Moving K-Means Algorithm and Fuzzy Min-Max Neural Network
Nov 10, 2013
DOI:
Published in: Journal of Theoretical and Applied Information Technology
Publisher: Little Lion
Anas Quteishat
Mohammad Al-Batah
Anwar Al-Mofleh
Sharhabeel Hassan Alnabelsi
Pap smear screening is the most successful attempt of medical science and practice for the early detection of cervical cancer. Manual analysis of the cervical cells is time consuming, laborious and error prone. This paper presents a Neural Network (NN) based system for classifying cervical cells as normal, low-grade squamous intra-epithelial lesion (LSIL) and high-grade squamous intra-epithelial lesion (HSIL). The system consists of three stages. In the first stage, cervical cells are segmented using the Adaptive Fuzzy Moving K-means (AFMKM) clustering algorithm. In the second stage, the feature extraction process is performed. In the third stage, the extracted data is classified using Fuzzy Min-Max (FMM) NN. The empirical results show that the proposed method can achieve acceptable results.