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Diagnostic classification of lung nodules using 3D neural networks

Raunak Dey, Zhongjie Lu, Yi Hong
2018 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)  
In this paper, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images, which aims to learn a direct mapping from 3D images to class labels.  ...  To achieve this goal, four two-pathway Convolutional Neural Networks (CNN) are proposed, including a basic 3D CNN, a novel multi-output network, a 3D DenseNet, and an augmented 3D DenseNet with multi-outputs  ...  However, most of existing deep learning models handle the pulmonary nodule classification problem by utilizing 2D convolutional neural networks (CNN) [6] or multiview 2D CNNs to mimic 3D image volumes  ... 
doi:10.1109/isbi.2018.8363687 dblp:conf/isbi/DeyLH18 fatcat:ltlhtosyoze6zh7ypleovarede

LDNNET: Towards Robust Classification of Lung Nodule and Cancer using Lung Dense Neural Network

Ying Chen, Yerong Wang, Fei Hu, Longfeng Feng, Taohui Zhou, Cheng Zheng
2021 IEEE Access  
[27] proposed a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer.  ...  These 3D images are better than 2D images in the classification of lung nodules.  ...  Author Name: Preparation of Papers for IEEE Access (February 2017)  ... 
doi:10.1109/access.2021.3068896 fatcat:b65ytjf34bgudotehv3jmfif74

Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network

Muhammad Bilal Zia, Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China, Juan Juan Zhao, Xiao Ning, Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China, Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
2020 International Journal of Hybrid Information Technology  
First, Improved 3D-Faster R-CNN with U-net like encoder and decoder is used for detection of nodule and then Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) is proposed for the pulmonary  ...  Lung nodule classification has been one of the major problem relevant to Computer-Aided Diagnosis (CAD) system.  ...  Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) Distinguishing between malignant and benign nodules based on small morphologicalfeatures is a very challenging and difficult task which needs  ... 
doi:10.21742/ijhit.2020.13.2.04 fatcat:3ozxxk55ffeaxgtjsjfyjilcay

Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function

Eali Stephen Neal Joshua, Debnath Bhattacharyya, Midhun Chakkravarthy, Hye-Jin Kim
2021 Traitement du signal  
The Gard Cam++ Class Activation Function is used with a squeeze-and-excite network to provide a revolutionary method for differentiating malignant from benign lung nodules on CT scans.  ...  The technology's objective is to aid radiologists in evaluating diagnostic data and differentiating benign from malignant lung nodules on CT images.  ...  Debnath Bhattacharya for thoroughly encouraging to conduct research on this topic.  ... 
doi:10.18280/ts.380421 fatcat:5e2sdd5ybjcwfdh6eju6tbjbsi

Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification

Yunpeng Wang, Lingxiao Zhou, Mingming Wang, Cheng Shao, Lili Shi, Shuyi Yang, Zhiyong Zhang, Mingxiang Feng, Fei Shan, Lei Liu
2020 Quantitative Imaging in Medicine and Surgery  
Therefore, we propose an automatic classification system for subcentimeter pulmonary adenocarcinoma, combining a convolutional neural network (CNN) and a generative adversarial network (GAN) to optimize  ...  Although computed tomography (CT) examinations are widely used in practice, it is still challenging and time-consuming for radiologists to distinguish between different types of subcentimeter pulmonary  ...  Computed tomography (CT), a widely used imaging technique in clinic, can provide internal lung information and facilitate the diagnosis of pulmonary adenocarcinoma.  ... 
doi:10.21037/qims-19-982 pmid:32550134 pmcid:PMC7276356 fatcat:27qzpb2hyfb5da4eimkpvgsmse

A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning

Wenkai Huang, Yihao Xue, Yu Wu, Yuchen Qiu
2019 PLoS ONE  
Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer.  ...  Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively.  ...  [13] proposed an automatic detection framework for pulmonary nodules based on a 2D-convolutional neural network.  ... 
doi:10.1371/journal.pone.0219369 pmid:31299053 pmcid:PMC6625700 fatcat:dpgfcau655d6hp22xqz45c4rfi

Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography

Ke Sun, Shouyu Chen, Jiabi Zhao, Bin Wang, Yang Yang, Yin Wang, Chunyan Wu, Xiwen Sun
2021 Frontiers in Oncology  
PurposeTo establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule  ...  (SPN) or mass (SPM) on computed tomography (CT).MethodA total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n  ...  An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification.  ... 
doi:10.3389/fonc.2021.792062 pmid:34993146 pmcid:PMC8724915 fatcat:ptouhegqjnbjhknqsmpho54lqu

Lung Nodule Malignancy Prediction from Longitudinal CT Scans with Siamese Convolutional Attention Networks

Benjamin P. Veasey, Justin Broadhead, Michael Dahle, Albert Seow, Amir Amini
2020 IEEE Open Journal of Engineering in Medicine and Biology  
Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure  ...  Results: Our results show that the proposed method outperforms a comparable 3-D network with less than half the parameters on single-time-point classification and further achieves performance gains on  ...  Recent CADx systems for lung nodule classification are dominated by deep learning strategies for feature extraction and classification. 2-D Convolutional Neural Networks (CNNs) [3, 4] , multi-view 2-D  ... 
doi:10.1109/ojemb.2020.3023614 pmid:35402947 pmcid:PMC8975149 fatcat:6vmh7b3tfbetxdcszo4tqnfzhq

Multi-scale pulmonary nodule classification with deep feature fusion via residual network

Guokai Zhang, Dandan Zhu, Xiao Liu, Mingle Chen, Laurent Itti, Ye Luo, Jianwei Lu
2018 Journal of Ambient Intelligence and Humanized Computing  
In this paper, we design a novel model based on convolution neural network to achieve automatic pulmonary nodule malignancy classification.  ...  Experimental results on the public Lung Image Database Consortium dataset demonstrate that our model can achieve a lung nodule classification accuracy of 87.5% which outperforms state-of-the-art methods  ...  Acknowledgements This work has been supported by the General Program of National Natural Science Foundation of China (NSFC) under Grant nos. 61572362, 81571347, and 61806147, the Central Universities under  ... 
doi:10.1007/s12652-018-1132-5 fatcat:epd5dyqud5bgphoudvgise7vs4

Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network

Xu YM, Zhang T, Xu H, Qi L, Zhang W, Zhang YD, Gao DS, Yuan M, Yu TF
2020 Cancer Management and Research  
of this study is to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience  ...  in detecting pulmonary nodules on thin-section computed tomography (CT).Patients and Methods: We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution  ...  Keywords: computer-aided detection, computed tomography, pulmonary nodules, convolutional neural network Introduction Lung cancer is the leading cause of cancer death worldwide. 1 On computed tomography  ... 
doaj:489bb6e7bac64fc39a6f5fbf263a69e1 fatcat:irywxyyn3zeslddnaer2evoejm

3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

Eali Stephen Neal Joshua, Debnath Bhattacharyya, Midhun Chakkravarthy, Yung-Cheol Byun, Hassène Gritli
2021 Journal of Healthcare Engineering  
The 3D convolutional neural network is able to make use of the full nonlinear 3D context information of lung nodule detection from the DICOM (Digital Imaging and Communications in Medicine) images, and  ...  So, in this paper, we explored the lung nodule classification using the improvised 3D AlexNet with lightweight architecture. Our network employed the full nature of the multiview network strategy.  ...  Acknowledgments is research was financially supported by the Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea, under the "Regional Specialized Industry Development Program  ... 
doi:10.1155/2021/6695518 pmid:33777347 pmcid:PMC7979307 fatcat:racp534ncbgovnjpg33nm27vqy

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification [article]

Shiwen Shen, Simon X. Han, Denise R. Aberle, Alex A.T. Bui, Willliam Hsu
2018 arXiv   pre-print
In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is  ...  The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy.  ...  Computing resources were provided by the NIH Data Commons Pilot and a donation of a TITAN Xp graphics card by the NVIDIA Corporation.  ... 
arXiv:1806.00712v1 fatcat:ttvhslgi2rds3fnr7qja3hu5by

Convolutional neural networks: an overview and application in radiology

Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi
2018 Insights into Imaging  
Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology  ...  Key Points • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including  ...  Acknowledgements We would like to acknowledge Yasuhisa Kurata, MD, PhD, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine.  ... 
doi:10.1007/s13244-018-0639-9 pmid:29934920 fatcat:vbo6znqwjbax7h425choj2ikwm

Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT [article]

Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
2021 arXiv   pre-print
of lung nodules in computed tomography (CT) image volumes.  ...  Furthermore, lung nodules are often heterogeneous in the cross-sectional image slices of a 3D volume.  ...  Acknowledgements This work was supported in part by Australian Research Council (ARC) grants (DP170104304 and IC170100022).  ... 
arXiv:2103.03931v2 fatcat:qxpewluygfbc5d4ueti5fun27i

Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning [chapter]

Sarfaraz Hussein, Kunlin Cao, Qi Song, Ulas Bagci
2017 Lecture Notes in Computer Science  
In this study, we propose a 3D Convolutional Neural Network (CNN) based nodule characterization strategy.  ...  Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis.  ...  Capitalizing on the significant progress of deep learning technologies for image classification and their potential applications in radiology [4] , we propose a 3D Convolutional Neural Network (CNN) based  ... 
doi:10.1007/978-3-319-59050-9_20 fatcat:oq23wasdgrggpevudeg5c5a3ei
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