Novel Approach to Computer-Aided Detection of Lung Nodules of Difficult Location with Use of Multifactorial Models and Deep Neural Networks

In recent years, the research into automatic aided detection systems for pulmonary nodules has been extremely active. Most of the existing studies are based on 2D convolution neural networks, which cannot make full use of computed tomography’s (CT) 3D spatial information. To address this problem, a computer-aided detection (CAD) system for lung nodules based on a 3D residual network (3D-ResNet) inspired by cognitive science is proposed in this paper. In this system, we feed the slice information extracted from three different axis planes into the U-NET network set, and make the joint decision to generate a candidate nodule set, which is the input of the proposed 3D residual network after extraction. We extracted 3D samples with 40, 44, 48, 52, and 56 mm sides from each candidate nodule in the candidate set and feed them into the trained residual network to get the probability of positive nodule after re-sampling the 3D sample to 48 × 48 × 48 mm 3 . Finally, a joint judgment is made based on the probabilities of five 3D samples of different sizes to obtain the final result. Random rotation and translation and data amplification technology are used to prevent overfitting during network training. The detection intensity on the largest public data set (i.e., the Lung Image Database Consortium and Image Database Resource Initiative—LIDC-IDRI) reached 86.5% and 92.3% at 1 frame per second (FPs) and 4 FPs respectively using our algorithm, which is better than most CAD systems using 2D convolutional neural networks. In addition, a 3D residual network and a multi-section 2D convolution neural network were tested on the unrelated Tianchi dataset. The results indicate that 3D-ResNet has better feature extraction ability than multi-section 2D-ConvNet and is more suitable for reduction of false positive nodules.

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Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2009.2017027 ◽

2009 ◽

Vol 56 (7) ◽

pp. 1810-1820 ◽

Cited By ~ 173

Author(s):

Xujiong Ye ◽

Xinyu Lin ◽

J. Dehmeshki ◽

G. Slabaugh ◽

G. Beddoe

Keyword(s):

Ct Images ◽

Lung Nodules ◽

Computer Aided Detection ◽

Thoracic Ct ◽

Computer Aided

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Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

Scientific Reports ◽

10.1038/s41598-021-88538-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

A. Wong ◽

Z. Q. Lin ◽

L. Wang ◽

A. G. Chung ◽

B. Shen ◽

...

Keyword(s):

Neural Networks ◽

Monte Carlo ◽

Lung Disease ◽

Disease Severity ◽

Deep Neural Networks ◽

Cross Validation ◽

X Rays ◽

Computer Aided ◽

Monte Carlo Cross Validation ◽

Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

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Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

Radiology and Medical Diagnostic Imaging ◽

10.31487/j.rdi.2020.02.05 ◽

2020 ◽

pp. 1-5

Author(s):

Ammar Chaudhry ◽

William H. Moore

Keyword(s):

Sensitivity And Specificity ◽

Chest Radiography ◽

Predictive Value ◽

Lung Nodule ◽

Chest Radiographs ◽

Lung Nodules ◽

Computer Aided Detection ◽

Cad System ◽

Computer Aided ◽

The Mean

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

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Computer-aided detection of lung nodules by SVM based on 3D matrix patterns

Clinical Imaging ◽

10.1016/j.clinimag.2012.02.003 ◽

2013 ◽

Vol 37 (1) ◽

pp. 62-69 ◽

Cited By ~ 21

Author(s):

Qingzhu Wang ◽

Wenwei Kang ◽

Chunming Wu ◽

Bin Wang

Keyword(s):

Lung Nodules ◽

Computer Aided Detection ◽

Computer Aided ◽

3D Matrix

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Computer-Aided Detection and Diagnosis of Lung Nodules Using CT Scan Images: An Analytical Review

10.1007/978-981-16-3346-1_44 ◽

2021 ◽

pp. 545-558

Author(s):

Nikhat Ali ◽

Jyotsna Yadav

Keyword(s):

Ct Scan ◽

Lung Nodules ◽

Computer Aided Detection ◽

Computer Aided ◽

Analytical Review ◽

Detection And Diagnosis

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Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190206153321 ◽

2020 ◽

Vol 16 (1) ◽

pp. 16-26 ◽

Cited By ~ 1

Author(s):

Shabana Rasheed Ziyad ◽

Venkatachalam Radha ◽

Thavavel Vayyapuri

Keyword(s):

Computed Tomography ◽

Review Paper ◽

Lung Nodule ◽

Lung Nodules ◽

Computer Aided Detection ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Computer Aided ◽

Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

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