Integrated System for Classification of Pulmonary Nodules on CT Images

Abstract

Mortality rate of lung cancer is increasing very day all over the world. Early stage lung nodules detection and proper treatment is solution to reduce the deaths due to lung cancer. In this research work proposed integrated CADe/CADx system segments and classifies lung nodules into benign or malignant. CADe phase segments Well Circumscribed Nodules (WCN), Juxta Vascular Nodules (JVN) and Juxta Pleural Nodules (JPN) of different size in diameter. This part uses algorithms proposed in our previous WCN, JVN and JPN lung nodules segmentation work. CADx performance classification of segmented WCNs, JVNs and JPNs nodules into benign or malignant. In first part of CADx system hybrid features of segmented lung nodules are extracted and features dimension vector is reduced with Linear Discrimination Analysis. Finally, Probabilistic Neural Network uses reduced hybrid features of segmented nodules to classify segmented nodules as benign or malignant. Proposed integrated system achieved high classification accuracy of 94.85 for WCNs, 97.65 for JVNs and 97.96 for JPNs of different size in diameter (nodules diameter< 10mm, nodules diameter >10mm and < 30mm, nodules diameter >30mm and <70mm). For small nodules achieved classification performance values are, accuracy of 94.85, sensitivity of 90 and specificity of 95.85. And nodules of size 10mm to 30mm obtained accuracy, sensitivity and specificity are 97.85, 97.65 and 94.15 respectively.