Bayesian classification and artificial neural network methods for lung cancer early diagnosis

Abstract

Lung cancer is a serious illness which can be cured if it is diagnosed at early stages. One technique which is commonly used for early detection of this type of cancer consists of analyzing sputum images. However, the analysis of sputum images is time consuming and requires highly trained personnel to avoid diagnostic errors. Image processing techniques provide a reliable tool for improving the manual screening of sputum samples. In this paper, we address the problem of extraction and segmentation the sputum cells based on the analysis of sputum color image with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we use a Bayesian classifier to extract the sputum cells followed by using a Hopfield Neural Network (HNN) to segment the extracted cells into nuclei and cytoplasm regions from the background region. The final results will be used for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer. We used some performance criteria such as sensitivity, specificity and accuracy to evaluate the proposed methods. Our methods are validated via a series of experimentation conducted with a data set of 88 images.