Lung Nodule Segmentation using Deep Learning and Advanced UNet Model

Abstract

Cancer is known as one of the world’s top reason of mortality in human beings. Lung cancer, notably, has the highest mortality rate. Thus, timely detection of nodule or tumor is a critical and significant job in saving lives. One of the hot topic in current research field is automatic detection of lung nodules. Many methods have been implemented using computer vision-based technologies in the past, but achieving the desired precision still remains a difficult job. In this research, we adopt Convolutional Neural Network (CNN) based UNet image segmentation model and improved its architecture by incorporating convolution mechanisms. Moreover, this scheme uses binary cross entropy as loss function during training process. The proposed mechanism is tested on LIDC-IDRI dataset. The experimental analysis shows the augmented performance of proposed approach when compared with existing segmentation techniques. The qualitative and quantitative comparative analysis shows that the suggested scheme substantially improves the efficiency of segmentation performance.