Abstract
Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript depicts an approach of finding lung nodule at an initial stage that comprises of three major phases: (1) lung nodule segmentation using Otsu threshold followed by morphological operation; (2) extraction of geometrical, texture and deep learning features for selecting optimal features; (3) The optimal features are fused serially for classification of lung nodule into two categories that is malignant and benign. The lung image database consortium image database resource initiative (LIDC-IDRI) is used for experimentation. The experimental outcomes show better performance of presented approach as compared with the existing methods.