A New System for Lung Cancer Diagnosis based on the Integration of Global and Local CT Features

Lung cancer leads deaths caused by cancer for both men and women worldwide, that is why creating systems for early diagnosis with machine learning algorithms and nominal user intervention is of huge importance. In this manuscript, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. This system integrates global and local features to give an implication of the nodule prior growth rate, which is the main point for diagnosis of pulmonary nodules. 3D adjustable local binary pattern and some basic geometric features are used to extract the nodule global features, and the local features are extracted using 3D convolutional neural networks (3D-CNN) because of its ability to exploit the spatial correlation of input data in an efficient way. Finally all these features are integrated using autoencoder to give a final diagnosis for the lung nodule whether benign or malignant. The system was evaluated using 727 nodules extracted from the Lung Image Database Consortium (LIDC) dataset. The proposed system diagnosis accuracy, sensitivity, and specificity were 92.20%,93.55%, and 91.20% respectively. The proposed framework demonstrated its promise as a valuable tool for lung cancer detection evidenced by its higher accuracy.

1. Introduction

Lung cancer is the main reason for cancer-related mortality not only in the United States but also all over the world. Its rate increases despite the extraordinary evolution of the imaging devices and careful hospitalization, because it is usually diagnosed at late stages [1]. The early diagnosis of lung cancer will decrease the mortality rate by helping doctors decide the ideal way for lung cancer treatment for every patient [2]. The Computed Tomography (CT) scans increases the survival rate by 20% [3]. The problem that faces CT scans in the detection of lung cancer is that it is a very difficult task for doctors, as it takes much time to be analyzed because of the mass of the data that saved in the CT screening. A lot of researchers are beginning to develop new frameworks for computer aided diagnostic (CADx) systems to help doctors analyze this huge amount of data [4]. The new trend of this research depends mainly on merging image processing techniques and machine learning techniques to help in analyzing these huge amounts of data accurately and fastly to improve the cancer detection and diagnosis task [5].