Research Topic

Automatic Lung Nodule Detection with Deep Learning

About this Research Topic

In recent years, deep learning and convolutional neural network (CNN) have been actively utilized in medical image analysis. For example, deep learning algorithms can be used for the differentiation of skin images and that of retinal fundus images. Previous studies regarding these images of skin and retinal fundus show that CNN performance can be equivalent to that of clinicians. The CNN model has also achieved excellent results in the detection, segmentation, and differentiation of lung nodules. For the purpose of early diagnosis of cancerous lung tumors, the detection, segmentation, and differentiation of lung nodules are fundamental steps. The National Lung Screening Trial (NLST) research study demonstrated that early diagnosis of lung cancer could improve prognosis by detecting lung nodules on low-dose Computed Tomography (CT) images. It is highly expected that CNNs may be beneficial in managing pulmonary nodules.

At present, the CNN algorithm has achieved high accuracy in detecting these nodules, and computational tools applying CNNs are being used clinically for this end. As a result, there is a pressing need for further research on CNN applications that not only detect but also support the overall management of lung nodules.

The goal of this Research Topic is to collect studies that have additional functions in the detection of lung nodules using deep learning and CNNs. We welcome high-quality submissions on any topic related to deep learning or CNN algorithms and image analysis of lung nodules, contributions with high technical novelty, or significant clinical results. For example, we will consider papers including but not limited to the following subtopics:

• Cutting-edge deep learning or CNN methodology/algorithms for lung nodule image investigation (detection, segmentation, differentiation, management, etc.);
• Clinical applications for lung nodule images: applications of deep learning or CNNs to analyze the longitudinal changes of lung nodules on CT images and risk assessment of lung cancer in patients with lung nodules;
• Open-source software or open data for deep learning or CNNs for lung nodule images analysis;
• Reproducibility/validation study of open-source software for deep learning or CNNs for lung nodules analysis;


Keywords: Convolutional Neural Networks, Deep Learning, Automatic Nodule Detection, Lung Cancer, Image Analysis


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In recent years, deep learning and convolutional neural network (CNN) have been actively utilized in medical image analysis. For example, deep learning algorithms can be used for the differentiation of skin images and that of retinal fundus images. Previous studies regarding these images of skin and retinal fundus show that CNN performance can be equivalent to that of clinicians. The CNN model has also achieved excellent results in the detection, segmentation, and differentiation of lung nodules. For the purpose of early diagnosis of cancerous lung tumors, the detection, segmentation, and differentiation of lung nodules are fundamental steps. The National Lung Screening Trial (NLST) research study demonstrated that early diagnosis of lung cancer could improve prognosis by detecting lung nodules on low-dose Computed Tomography (CT) images. It is highly expected that CNNs may be beneficial in managing pulmonary nodules.

At present, the CNN algorithm has achieved high accuracy in detecting these nodules, and computational tools applying CNNs are being used clinically for this end. As a result, there is a pressing need for further research on CNN applications that not only detect but also support the overall management of lung nodules.

The goal of this Research Topic is to collect studies that have additional functions in the detection of lung nodules using deep learning and CNNs. We welcome high-quality submissions on any topic related to deep learning or CNN algorithms and image analysis of lung nodules, contributions with high technical novelty, or significant clinical results. For example, we will consider papers including but not limited to the following subtopics:

• Cutting-edge deep learning or CNN methodology/algorithms for lung nodule image investigation (detection, segmentation, differentiation, management, etc.);
• Clinical applications for lung nodule images: applications of deep learning or CNNs to analyze the longitudinal changes of lung nodules on CT images and risk assessment of lung cancer in patients with lung nodules;
• Open-source software or open data for deep learning or CNNs for lung nodule images analysis;
• Reproducibility/validation study of open-source software for deep learning or CNNs for lung nodules analysis;


Keywords: Convolutional Neural Networks, Deep Learning, Automatic Nodule Detection, Lung Cancer, Image Analysis


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Topic Editors

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Submission Deadlines

30 June 2021 Abstract
30 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 June 2021 Abstract
30 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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