scholarly journals Multi-Dimension and Multi-Feature Hybrid Learning Network for Classifying the Sub Pathological Type of Lung Nodules through LDCT

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2734
Author(s):  
Jiacheng Fan ◽  
Jianying Bao ◽  
Jianlin Xu ◽  
Jinqiu Mo
Keyword(s):  
Lung Nodule ◽  
Hybrid Learning ◽  
Routine Screening ◽  
Lung Nodules ◽  
Feature Maps ◽  
Appropriate Treatment ◽  
3D Network ◽  
Residual Block ◽  
2D And 3D ◽  
2D Network

In order to develop appropriate treatment and rehabilitation plans with regard to different subpathological types (PILs and IAs) of lung nodules, it is important to diagnose them through low-dose spiral computed tomography (LDCT) during routine screening before surgery. Based on the characteristics of different subpathological lung nodules expressed from LDCT images, we propose a multi-dimension and multi-feature hybrid learning neural network in this paper. Our network consists of a 2D network part and a 3D network part. The feature vectors extracted from the 2D network and 3D network are further learned by XGBoost. Through this formation, the network can better integrate the feature information from the 2D and 3D networks. The main learning block of the network is a residual block combined with attention mechanism. This learning block enables the network to learn better from multiple features and pay more attention to the key feature map among all the feature maps in different channels. We conduct experiments on our dataset collected from a cooperating hospital. The results show that the accuracy, sensitivity and specificity of our network are 83%, 86%, 80%, respectively It is feasible to use this network to classify the subpathological type of lung nodule through routine screening.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2547 ◽  
Author(s):  
Wenxin Dai ◽  
Yuqing Mao ◽  
Rongao Yuan ◽  
Yijing Liu ◽  
Xuemei Pu ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Feature Representation ◽  
Feature Maps ◽  
Sar Images ◽  
Feature Map ◽  
The Public ◽  
Single Feature ◽  
Great Performance ◽  
Residual Block ◽  
Fusion Feature

Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom–up and top–down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application.

scholarly journals Application of Deep Learning in Lung Cancer Imaging Diagnosis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Wenfa Jiang ◽  
Ganhua Zeng ◽  
Shuo Wang ◽  
Xiaofeng Wu ◽  
Chenyang Xu
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Malignant Tumors ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
X Rays ◽  
Imaging Diagnosis ◽  
Risk Of Cancer

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

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

2020 ◽  
pp. 1-5
Author(s):  
Ammar Chaudhry ◽  
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.

scholarly journals Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules’ Detection in a Phantom

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Cleverson Alex Leitão ◽  
Gabriel Lucca de Oliveira Salvador ◽  
Priscilla Tazoniero ◽  
Danny Warszawiak ◽  
Cristian Saievicz ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Ultralow Dose ◽  
Thermoluminescent Dosimeters ◽  
Significant Difference ◽  
Nodule Detection ◽  
The Difference ◽  
Conventional Ct

Background. The effects of dose reduction in lung nodule detection need better understanding. Purpose. To compare the detection rate of simulated lung nodules in a chest phantom using different computed tomography protocols, low dose (LD), ultralow dose (ULD), and conventional (CCT), and to quantify their respective amount of radiation. Materials and Methods. A chest phantom containing 93 simulated lung nodules was scanned using five different protocols: ULD (80 kVp/30 mA), LD A (120 kVp/20 mA), LD B (100 kVp/30 mA), LD C (120 kVp/30 mA), and CCT (120 kVp/automatic mA). Four chest radiologists analyzed a selected image from each protocol and registered in diagrams the nodules they detected. Kruskal–Wallis and McNemar’s tests were performed to determine the difference in nodule detection. Equivalent doses were estimated by placing thermoluminescent dosimeters on the surface and inside the phantom. Results. There was no significant difference in lung nodules’ detection when comparing ULD and LD protocols ( p = 0.208 to p = 1.000 ), but there was a significant difference when comparing each one of those against CCT ( p < 0.001 ). The detection rate of nodules with CT attenuation values lower than −600 HU was also different when comparing all protocols against CCT ( p < 0.001 to p = 0.007 ). There was at least moderate agreement between observers in all protocols (κ-value >0.41). Equivalent dose values ranged from 0.5 to 9 mSv. Conclusion. There is no significant difference in simulated lung nodules’ detection when comparing ULD and LD protocols, but both differ from CCT, especially when considering lower-attenuating nodules.

Designing 2D and 3D Network-on-Chip Architectures

2014 ◽  
Author(s):  
Konstantinos Tatas ◽  
Kostas Siozios ◽  
Dimitrios Soudris ◽  
Axel Jantsch
Keyword(s):  
Network On Chip ◽  
3D Network ◽  
On Chip ◽  
2D And 3D

A Novel Approach for Computer-Aided Diagnosis for Distinction Between Benign and Malignant of Lung Nodules Based on Machine Learning Techniques

2018 ◽  
pp. 281-290
Author(s):  
Shashidhara Bola
Keyword(s):  
False Positive Rate ◽  
Lung Nodule ◽  
Machine Learning Techniques ◽  
Lung Nodules ◽  
Data Set ◽  
Novel Approach ◽  
Learning Techniques ◽  
Nodule Shape ◽  
Positive Rate ◽  
Aided Diagnosis

A new method is proposed to classify the lung nodules as benign and malignant. The method is based on analysis of lung nodule shape, contour, and texture for better classification. The data set consists of 39 lung nodules of 39 patients which contain 19 benign and 20 malignant nodules. Lung regions are segmented based on morphological operators and lung nodules are detected based on shape and area features. The proposed algorithm was tested on LIDC (lung image database consortium) datasets and the results were found to be satisfactory. The performance of the method for distinction between benign and malignant was evaluated by the use of receiver operating characteristic (ROC) analysis. The method achieved area under the ROC curve was 0.903 which reduces the false positive rate.

The role of the geoid in one-, two-, and three-dimensional network adjustments

CISM journal ◽  
1990 ◽  
Vol 44 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Michael G. Sideris
Keyword(s):  
Three Dimensional ◽  
One Dimensional ◽  
Control Networks ◽  
Deflections Of The Vertical ◽  
Dimensional Network ◽  
3D Network ◽  
Computational Procedures ◽  
The One ◽  
2D And 3D

The geoid and its horizontal derivatives, the deflections of the vertical, play an important role in the adjustment of geodetic networks. In the one-dimensional (1D) case, represented typically by networks of orthometric heights, the geoid provides the reference surface for the measurements. In the two-dimensional (2D) adjustment of horizontal control networks, the geoidal undulations N and deflections of the vertical ξ, η are needed for the reduction of the measured quantities onto the reference ellipsoid. In the three-dimensional (3D) adjustment, N and ξ, η are basically required to relate geodetic and astronomic quantities. The paper presents the major gravimetric methods currently used for predicting ξ, η and N, and briefly intercompares them in terms of accuracy, efficiency, and data required. The effects of N, ξ, η on various quantities used in the ID, 2D, and 3D network adjustments are described explicitly for each case and formulas are given for the errors introduced by either neglecting or using erroneous N, ξ, η in the computational procedures.

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