Diagnosis of Lung Disorder Using Immune Genetic Algorithm and Fuzzy logic to Handle Incertitude

Abstract

In this paper, we present an immune based fuzzy-logic approach for computer-aided diagnosis scheme in medical imaging. The scheme applies to lung CT images and to detect and classify lung nodules. Classification of lung tissue is a significant and challenging task in any computer aided diagnosis system. This paper presents a technique for classification of lung tissue from computed tomography of the lung using the Gaussian interval type-2 fuzzy logic system. The type-2 Gaussian membership functions (T2MFs) and their footprint of uncertainty (FOU) are tuned by immune, genetic algorithm, which is the combination of immune genetic algorithm (GA) and local exploration operator. An immune, genetic algorithm estimates the parameters of the type-2 fuzzy membership function (T2MF). By using immune, genetic algorithm, converging speed is increased. The proposed local exploration operator helps in finding the best Gaussian distribution curve of a particular feature which improves the efficiency and accuracy of the diagnosis system.