Pulmonary Nodule Detection Based on Three-Dimensional Multiscale Convolutional Neural Network with Channel and Spatial Attention

2021 ◽  
Vol 11 (6) ◽  
pp. 1551-1559
Author(s):  
Yudu Zhao ◽  
Jun Ma ◽  
Zhenwei Peng ◽  
Hao Xia ◽  
Honglin Wan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spatial Attention ◽  
Pulmonary Nodule ◽  
Lung Nodule ◽  
Three Dimensional ◽  
Great Success ◽  
Position Information ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Early screening for pulmonary nodules is currently an important means for reducing lung cancer mortality. In recent years, three-dimensional convolutional neural networks have achieved great success in the field of pulmonary nodule detection. This paper proposes a pulmonary nodule detection method based on a threedimensional multiscale convolutional neural network with channel and spatial attention. First, a multiscale module is designed to extract the image features at different scales. Second, a channel and spatial attention module is designed to mine the correlation information between features from the perspective of space and channel. Then the extracted features are sent to a pyramid-like fusion mechanism, so that the features contain both deep semantic information and shallow position information, which is conducive to object positioning and bounding box regression. In general, the experiments on the LUng Nodule Analysis 2016 (LUNA16) dataset show that the average free-response receiver operating characteristic (FROC) score is 0.846. Compared with other current advanced methods, the method is competitive and effective.

scholarly journals The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 207 ◽  
Author(s):  
Dana Li ◽  
Bolette Mikela Vilmun ◽  
Jonathan Frederik Carlsen ◽  
Elisabeth Albrecht-Beste ◽  
Carsten Ammitzbøl Lauridsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Pulmonary Nodule ◽  
Image Database ◽  
Ct Scans ◽  
Learning Technology ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
And Training

The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

scholarly journals A robust convolutional neural network for lung nodule detection in the presence of foreign bodies

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuel Schultheiss ◽  
Sebastian A. Schober ◽  
Marie Lodde ◽  
Jannis Bodden ◽  
Juliane Aichele ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Foreign Bodies ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection

A Quantitative Method for Differentiating Malignant and Benign Pulmonary Nodules

2014 ◽  
Vol 513-517 ◽  
pp. 3830-3834
Author(s):  
Yu Zhao ◽  
Sheng Dong Nie ◽  
Jie Wu ◽  
Yuan Jun Wang
Keyword(s):  
Pulmonary Nodule ◽  
Quantitative Method ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Lung Nodule Detection ◽  
Objective Standard ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Definition Of ◽  
Important Basis

It is common sense that CAD has great significance in the lung nodule detection. But it is still controversial whether the CAD can also automatically differentiates between malignant and benign pulmonary nodules. The primary cause of this controversy is due to the subjective definition of 9 characteristics of nodules which are important basis of nodule identification. In other word, these characteristics are too dependent on the doctor scoring, and no objective standard of them has built which make these characteristics can be obtained by calculation.The main aim of this paper is to establish a quantitative method of the characteristics and refine these nine characteristics. This new method is used to find the objective replacement (a series features which can be measured through algorithms) of these subjective characteristics of the pulmonary nodule detection with Bayesian analysis.The experiment of our method proves that it is feasible to substitute the features of Pulmonary Nodule obtained by calculating for the characteristics of the nodule which only used to be gotten by the subjective judgment of doctors.

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