Comparison of Hopfield Neural Network and mean shift algorithm in segmenting sputum color images for lung Cancer Diagnosis

Abstract

Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. For this reason, we attempt to come with a computer aided diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color images. Therefore, the CAD system can play a significant role in the early detection of lung cancer. This paper, presents a comparison between two segmentation methods, a Hopfield Neural Network (HNN) and a mean shift clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The two methods are designed to classify the image of N pixels among M classes. In this study, we used 100 sputum color images to test both methods. We used some performance criteria such as recall, precision, and accuracy to evaluate the proposed methods and the mean shift algorithm has shown a better segmentation performance compared to the HNN.