Rule based classification of sputum images for early lung cancer detection

Abstract

Lung cancer is one of the most serious diseases in the world with the smallest survival rate after diagnosis, and with a progressive increase in the number of deaths each year. Survival from lung cancer is directly related to its growth at its detection time. The early detection and treatment of lung cancer can greatly improve the survival rate of patients. We attempt to develop a computer aided diagnosis (CAD) system for early detection of lung cancer based on the analysis of the sputum color images. We need to resolve several problems to complete the development of such system. Classification is one of the important problems to be considered. In this paper, we present a feature extraction process followed by a rule based classification technique to classify the sputum cell into cancerous or normal cell. We used 100 sputum color images to test the rule based method. The performance criteria such as sensitivity, precision, specificity and accuracy were used to evaluate the proposed techniques. The evaluation demonstrated the advantages of the new technique.