scholarly journals Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 46033-46044 ◽  
Author(s):  
Jun Wang ◽  
Jiawei Wang ◽  
Yaofeng Wen ◽  
Hongbing Lu ◽  
Tianye Niu ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Chest Ct ◽  
Ct Scans ◽  
Adaptive Detection ◽  
Nodule Size ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Pulmonary Nodule Detection using Chest Ct Images

2003 ◽  
Vol 44 (3) ◽  
pp. 252-257 ◽  
Author(s):  
D.-Y. Kim ◽  
J.-H. Kim ◽  
S.-M. Noh ◽  
J.-W. Park
Keyword(s):  
Pulmonary Nodule ◽  
Ct Images ◽  
Chest Ct ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

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.

Pediatric chest CT radiation dose reduction: protocol refinement based on noise injection for pulmonary nodule detection accuracy

2013 ◽  
Vol 37 (2) ◽  
pp. 334-341 ◽  
Author(s):  
Teresa Chapman ◽  
Jonathan O. Swanson ◽  
Grace S. Phillips ◽  
Marguerite T. Parisi ◽  
Adam M. Alessio
Keyword(s):  
Radiation Dose ◽  
Dose Reduction ◽  
Pulmonary Nodule ◽  
Chest Ct ◽  
Detection Accuracy ◽  
Nodule Detection ◽  
Noise Injection ◽  
Pediatric Chest ◽  
Ct Radiation ◽  
Pulmonary Nodule Detection

A multiscale Laplacian of Gaussian filtering approach to automated pulmonary nodule detection from whole-lung low-dose CT scans

2009 ◽  
Author(s):  
Sergei V. Fotin ◽  
Anthony P. Reeves ◽  
Alberto M. Biancardi ◽  
David F. Yankelevitz ◽  
Claudia I. Henschke
Keyword(s):  
Pulmonary Nodule ◽  
Low Dose ◽  
Ct Scans ◽  
Gaussian Filtering ◽  
Low Dose Ct ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Filtering Approach

scholarly journals A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4301 ◽  
Author(s):  
Jianning Chi ◽  
Shuang Zhang ◽  
Xiaosheng Yu ◽  
Chengdong Wu ◽  
Yang Jiang
Keyword(s):  
Pulmonary Nodule ◽  
Receptive Fields ◽  
Lung Parenchyma ◽  
Chest Ct ◽  
Detection Methods ◽  
Computer Assisted ◽  
Ct Image ◽  
Dilated Convolution ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the “suspicious nodules” in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the “coarse-to-fine” detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity.

scholarly journals A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 131 ◽  
Author(s):  
Shimaa EL-Bana ◽  
Ahmad Al-Kabbany ◽  
Maha Sharkas
Keyword(s):  
Pulmonary Nodule ◽  
Semantic Segmentation ◽  
Ct Scans ◽  
Computational Time ◽  
Dice Coefficient ◽  
Two Stage ◽  
Second Stage ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
The Impact

This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. Due to its crucial role in the early diagnosis of lung cancer, PND has considerable potential in improving the survival rate of patients. We propose a two-stage framework that exploits the ever-growing advances in deep neural network models, and that is comprised of a semantic segmentation stage followed by localization and classification. We employ the recently published DeepLab model for semantic segmentation, and we show that it significantly improves the accuracy of nodule detection compared to the classical U-Net model and its most recent variants. Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. We present the impact of various model training parameters and the computational time on the detection accuracy, featuring a 79.1% mean intersection-over-union (mIoU) and an 88.34% dice coefficient. This represents a mIoU increase of 60% and a dice coefficient increase of 30% compared to U-Net. The second stage involves feeding the output of the DeepLab-based semantic segmentation to a localization-then-classification stage. The second stage is realized using Faster RCNN and SSD, with an Inception-V2 as a backbone. On LUNA16, the two-stage framework attained a sensitivity of 96.4%, outperforming other recent models in the literature, including deep models. Finally, we show that adopting a transfer learning approach, particularly, the DeepLab model weights of the first stage of the framework, to infer binary (malignant-benign) labels on the Kaggle dataset for pulmonary nodules achieves a classification accuracy of 95.66%, which represents approximately 4% improvement over the recent literature.

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