Improvised grasshopper algorithm for automatic lung disease detection

Abstract

Chronic obstructive pulmonary disease (COPD) has been impacting a large population. It has a higher fatality rate than that of lung cancer. Diagnosis of this disease is quite challenging. Medical images analysis has been able to solve this challenge by early and accurate diagnosis of pulmonary disease. This analysis technique helps in pre-diagnosis and providing timely medical treatment thus reducing the mortality rate. The goal of this study is to establish an accurate process for classifying CT scan images into healthy lungs, COPD and Fibrosis impacted lung images. This classifying process has three steps. In the first step, lung scan is used for feature extraction. Then second and third step of feature selection and lung disease identification are carried using Machine Learning (ML) classifier. Haralick texture features with Gray Level Co-occurrence Matrix (GLCM), Zernike’s moments, Gabor features and spatial domain features are used for feature extraction from the segmented lung CT images. For feature selection, our proposed evolutionary algorithm is the Improvised Grasshopper Algorithm (IGOA). After feature extraction from CT scan medical images, IGOA selects an optimal set of features that increases the classification accuracy and decreases the cost of computation. Lastly, three ML classifiers viz. Decision Tree Classifier, k-Nearest Neighbor (KNN), Random Forest Classifier are applied to every feature set chosen by IGOA. The research results show that IGOA filtered out the maximum number of unimportant features of about 71.01%. IGOA eliminates 28.99% of the total extracted features. IGOA gave a better accuracy of 99.8%. Research results imply that the introduced feature selection method is appropriate for disease classification from CT scan images. IGOA method can be used for real-time applications as it has a less computational cost and has better accuracy.