Publication: Development of an intelligent system for classification of cervical precancerous stage using artificial neural network based on thinprep images
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Date
2008-04-01
Authors
Low, Aik Wei
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Abstract
Throughout the years, many researchers have been conducted on the potential applications of Artificial Intelligence (AI) in medical field. The purpose of this project is to develop an intelligent system to classify cervical pre-cancerous stage into normal, Low grade Squamous Intra-epithelial Lesion (LSIL), or High grade Squamous Intra-epithelial Lesion (HSIL) based on the features obtained from ThinPrep Images. The capability and suitability of neural networks as intelligent classification will be investigated. In this project, conventional Multilayered Perceptron (MLP) network and Hybrid Multilayered Perceptron (HMLP) network will be developed and their performances are compared to yield the most suitable network that will be used to model the classification system. The MLP will be developed using MATLAB and trained with Back Propagation, Levenberg Marquardt, and Bayesian Regularization learning algorithms. While the HMLP with Modified Recursive Prediction Error learning algorithm will be developed using Borland C++ Builder. The performance of neural networks was done based on accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The final result shows the HMLP network performed better classification when compare with the MLP. HMLP was able to achieve 96.98% of accuracy, 97.16% of sensitivity, 95.65% of specificity, 99.42% of positive predictive value, and 81.48% of negative predictive value. The final product of this project is a software system that is capable to classify cervical pre-cancerous stage with high accuracy, high applicability, fast and cheap.