Lung Nodule Detection and Analysis using VDE Chest Radiographs

Anoop, C S; Preeja,

doi:10.5120/20293-2704

IJCA

2015

DOI: 10.5120/20293-2704

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Lung Nodule Detection and Analysis using VDE Chest Radiographs

C S Anoop¹,

Preeja Preeja²

Abstract: Lung cancer becomes very common in our environment. Many computer-aided detection (CADe) schemes are available to detect lung nodules. So as to detect nodules in cost effective way, system uses chest radiographs (CXRs). Major challenge in those systems are the anatomical structures (ribs and clavicles) in the CXRs. These structures will conceal the nodules behind it. In order to overcome this problem virtual dual energy (VDE) technique has been implemented, which produces ribs and clavicle suppressed CXR image… Show more

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A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Cao

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, robust nodule detection has been a challenging task. In this study, we propose a two-stage convolutional neural network (TSCNN) architecture for lung nodule detection. The CNN architecture in the first stage is based on the improved UNet segmentation network to establish an initial detection of lung nodules. Simultaneously, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a novel sampling strategy, and use the offline hard mining idea for training and prediction according to the proposed cascaded prediction method. The CNN architecture in the second stage is based on the proposed dual pooling structure, which is built into three 3D CNN classification networks for false positive reduction.Since the network training requires a significant amount of training data, we adopt a data augmentation method based on random mask. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. The proposed method has been experimentally verified on the LUNA dataset.Experimental results show that the proposed TSCNN architecture can obtain competitive detection performance. Keywords: lung nodule detection; UNet; 3D CNN; ensemble learning;computer-aided diagnosis methods in the following aspects: 1) using the improved UNet segmentation model for lung nodule detection; 2) for the segmentation model training, we propose a new sampling strategy and an offline hard mining training approach; 3) we propose a cascade prediction method different from the traditional prediction method; 4) build three 3D CNN classification networks based on the dual pooling method; 5) design a data augmentation method based on random mask. MethodsThe lung nodule detection framework proposed in this paper is divided into two stages. The first stage: the detection of candidate nodules, which is based on the UNet architecture to achieve the detection of candidate nodules by segmenting suspicious nodules. The second stage: the reduction of false positive nodules, which is based on the 3DCNN architecture to eliminate false positive nodules through the integration of multiple models. The overall architecture of the proposed lung nodule detection method is shown in Fig. 2.

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A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Cao

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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show abstract

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Lung Nodule Detection and Analysis using VDE Chest Radiographs

Cited by 1 publication

References 18 publications

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

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