Pulmonary Nodule Detection Using V-Net and High-Level Descriptor Based SVM Classifier

Yuyun Ye,Miao Tian,Qiyu Liu,Heng-Ming Tai

doi:10.1109/access.2020.3026168

Abstract

Early detection of the pulmonary nodule is critical to increase the five-year survival rate of lung cancer. Many computer-aided diagnosis (CAD) systems have been proposed for nodule detection to assist radiologists in diagnosis. Along this direction, this paper proposes a novel automated pulmonary nodule detection model using the modified V-Nets and a high-level descriptor based support vector machine (SVM) classifier. The former is for nodule candidate detection and the latter is for false positive (FP) reduction. A hard mining scheme for retraining is devised to improve the FP reduction performance. The proposed SVM classifier, which employs more critical features of CT images, performs superior in FP reduction than other SVM based classifiers and CNN classifiers. Experimental results using the LIDC-IDRI dataset are presented to demonstrate the effectiveness of the proposed CAD model.

Highlights

Pulmonary cancer is a leading cause of cancer death in the world
The V-Net, on the other hand, employs a loss function based on dice coefficients to optimize the training process and a residual learning scheme is incorporated into the model to improve the performance [29]
computed tomography (CT) scan images used for test are from Luna16 dataset [35]

Summary

INTRODUCTION

Pulmonary cancer is a leading cause of cancer death in the world. Global cancer statistics reported that nearly 1.83 million new cases of lung cancer occurred and the estimated annual deaths are over 1.5 million [1]. The first stage usually allows to have very high sensitivity which comes with the cost of many false positive cases The latter process is designed to reduce the number of FPs. Conventional pulmonary nodule detection methods usually adopt complicated image processing algorithms, such as image transformation, enhancement, and segmentation, to detect the lung parenchyma, select nodule candidates, extract distinctive features, and perform the false positive reduction. Conventional pulmonary nodule detection methods usually adopt complicated image processing algorithms, such as image transformation, enhancement, and segmentation, to detect the lung parenchyma, select nodule candidates, extract distinctive features, and perform the false positive reduction Owing to their heavy dependence on hand-crafted features, these conventional methods still have room for improvement [4]. This paper proposes a pulmonary nodule detection model using the modified V-Net and a high-level descriptor based SVM classifier.

RELATED WORKS

NODULE CANDIDATE DETECTION

CONCLUSION