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Lung Nodule Classification using Deep Local-Global Networks [article]

Mundher Al-Shabi, Boon Leong Lan, Wai Yee Chan, Kwan-Hoong Ng, Maxine Tan
2019 arXiv   pre-print
Conclusions: Our proposed Deep Local-Global network has the capability to accurately extract both local and global features.  ...  and structure of the nodule using a local feature extractor.  ...  We proposed using a novel method called Local-Global neural network for lung nodule classification 2.  ... 
arXiv:1904.10126v1 fatcat:7vcoczjj2vahja76l6x2wgdltq

Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification

Keming Mao, Renjie Tang, Xinqi Wang, Weiyi Zhang, Haoxiang Wu
2018 Complexity  
First, lung nodule images are divided into local patches with Superpixel. Then these patches are transformed into fixed-length local feature vectors using unsupervised deep autoencoder (DAE).  ...  The visual vocabulary is constructed based on the local features and bag of visual words (BOVW) is used to describe the global feature representation of lung nodule image.  ...  The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of GPU used for this research.  ... 
doi:10.1155/2018/3078374 fatcat:24pjgn2kl5ewlkndouprcrrnza

3D Axial-Attention for Lung Nodule Classification [article]

Mundher Al-Shabi, Kelvin Shak, Maxine Tan
2021 arXiv   pre-print
Purpose: In recent years, Non-Local based methods have been successfully applied to lung nodule classification.  ...  Methods: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network.  ...  Many deep learning-based Computer-Aided Diagnosis (CAD) systems have been introduced for lung nodule classification [3] [4] [5] [6] [7] . Al-Shabi et al.  ... 
arXiv:2012.14117v2 fatcat:ecmtk3j3zzdurjysl2xxutxlba

Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

Panpan Wu, Xuanchao Sun, Ziping Zhao, Haishuai Wang, Shirui Pan, Björn Schuller
2020 Computational Intelligence and Neuroscience  
In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed.  ...  in lung nodule detection, causing low classification accuracy and high false positive rate.  ...  in using CNNs for lung nodule classification tasks. e residual network proposed by He et al  ... 
doi:10.1155/2020/8975078 pmid:32318102 pmcid:PMC7149413 fatcat:kx2frtwld5eghoajbbdvpvlxrq

Weighing features of lung and heart regions for thoracic disease classification

Jiansheng Fang, Yanwu Xu, Yitian Zhao, Yuguang Yan, Junling Liu, Jiang Liu
2021 BMC Medical Imaging  
Conclusion We propose a novel deep framework for the multi-label classification of thoracic diseases in chest X-ray images.  ...  Compared to existing methods fusing global and local features, we adopt feature weighting to avoid weakening visual cues unique to lung and heart regions.  ...  The deep fusion network unifying global and local features is gradually popular in computer vision tasks [36, 37] .  ... 
doi:10.1186/s12880-021-00627-y pmid:34112095 pmcid:PMC8194196 fatcat:sa3dyneanratzlfc2inpmzmj3u

Multi-Level Cross Residual Network for Lung Nodule Classification

Juan Lyu, Xiaojun Bi, Sai Ho Ling
2020 Sensors  
nodules) of lung nodules, respectively.  ...  In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies.  ...  However, they only applied 686 nodule samples for both training and testing processes. In 2019, a deep local-global network was proposed for lung nodule classification [20] .  ... 
doi:10.3390/s20102837 pmid:32429401 fatcat:wfgbc4r3bzcwlgz7hasv4yz4na

Class-Aware Adversarial Lung Nodule Synthesis in CT Images [article]

Jie Yang, Siqi Liu, Sasa Grbic, Arnaud Arindra Adiyoso Setio, Zhoubing Xu, Eli Gibson, Guillaume Chabin, Bogdan Georgescu, Andrew F. Laine, Dorin Comaniciu
2018 arXiv   pre-print
We show that combining the real image patches and the synthetic lung nodules in the training set can improve the mean AUC classification score across different network architectures by 2%.  ...  By evaluating on the public LIDC-IDRI dataset, we demonstrate an example application of the proposed framework for improving the accuracy of the lung nodule malignancy estimation as a binary classification  ...  Class-Aware Synthesis Two discriminator networks D local and D global are used to optimize G 1 and G 2 in an adversarial approach together with the reconstruction losses.  ... 
arXiv:1812.11204v1 fatcat:ti3ykgz6irdhvptzwzuieasyw4

Weighing Features of Lung and Heart Regions for Thoracic Disease Classification [article]

Jiansheng Fang, Yanwu Xu, Yitian Zhao, Yuguang Yan, Junling Liu, Jiang Liu
2021 arXiv   pre-print
Compared to existing methods fusing global and local features, we adopt feature weighting to avoid weakening visual cues unique to lung and heart regions.  ...  Inspired by this, we propose a novel deep learning framework to explore discriminative information from lung and heart regions.  ...  The deep fusion network unifying global and local features is gradually popular in computer vision tasks [36, 37] .  ... 
arXiv:2105.12430v1 fatcat:ewpaulgqkfa6dds6hinjkua7z4

Computer-aided detection in chest radiography based on artificial intelligence: a survey

Chunli Qin, Demin Yao, Yonghong Shi, Zhijian Song
2018 BioMedical Engineering OnLine  
[111] proposed the attention guided convolutional neural network (AG-CNN), which has three branches, i.e., global branch, local branch, and fusion branch.  ...  [45] first proposed a method of lung field segmentation using features. The features used were gray-scale, a measure of the local difference, and a measure of the local texture.  ... 
doi:10.1186/s12938-018-0544-y pmid:30134902 fatcat:moshts5kpjd4hpejcs2irwf6eq

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Rui Li, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, Bingding Huang
2022 Diagnostics  
The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well.  ...  Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture.  ...  [73] proposed combining a deep local-global network with residual and non-local blocks to extract the global features with few parameters.  ... 
doi:10.3390/diagnostics12020298 pmid:35204388 pmcid:PMC8871398 fatcat:zbasqznr5vblnkfmeuzwlmqbom

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)  
This is demonstrated on our dataset with encouraging prediction accuracy in lung nodule classification.  ...  In particular, the 3D multi-output DenseNet (MoDenseNet) achieves the state-of-the-art classification accuracy on the task of end-to-end lung nodule diagnosis.  ...  An accurate diagnosis requires both local detailed information of a lung nodule and global surrounding tissues for comparison.  ... 
doi:10.1109/isbi.2018.8363687 dblp:conf/isbi/DeyLH18 fatcat:ltlhtosyoze6zh7ypleovarede

Study on the detection of pulmonary nodules in CT images based on deep learning

Gai Li, Wei Zhou, Weibin Chen, Fengtao Sun, Yu Fu, Fengling Gong, Huiying Zhang
2020 IEEE Access  
The algorithm can help us to locate the lung nodules with higher accuracy.  ...  Then, the convolution neural network (CNN) optimized by genetic algorithm and the traditional CNN are used to extract the features of CT image of pulmonary nodules.  ...  deep learning model to study the detection and classification method of lung nodules based on the deep convolutional neural network.  ... 
doi:10.1109/access.2020.2984381 fatcat:kbgvx2thfjfcrobocl5esddhwa

The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification [article]

Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loic Le Folgoc, Octavio E. Martinez Manzanera, Sam Ellis, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel
2021 arXiv   pre-print
In lung nodule classification, for example, many works report results on the publicly available LIDC dataset.  ...  Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.  ...  The Local-Global network was proposed by [1] and consists of two blocks.  ... 
arXiv:2108.05386v1 fatcat:l7ahpm25jzg4zjoub2rhppwrxu

Application of artificial intelligence in respiratory medicine

Chunxi Zhang, Weijin Wu, Jia Yang, Jiayuan Sun
2022 Journal of Digital Health  
nodules, classification of benign/malignant pulmonary nodules and intrathoracic lymph nodes, classification of lung cancer pathological images, and lung cancer prognosis analysis.  ...  In summary, artificial intelligence is widely used in the auxiliary diagnosis of respiratory diseases, and has a great potential to become a valuable assistant to respiratory physicians in the near future  ...  [26] used Gabor wavelets to extract the texture features of solitary pulmonary nodules from a frequency angle and deep belief networks to perform benignancy/malignancy classification of pulmonary nodules  ... 
doi:10.55976/jdh.1202215330-39 fatcat:wombzyo5zvd2hcohqrj7qy4cau

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Zhiqian Lu, Feixiang Long, Xiaodong He, Kelvin Wong
2022 Computational and Mathematical Methods in Medicine  
The depth neural network and 3D convolution neural network were used to construct the model and train the classification and segmentation algorithm.  ...  The depth neural network algorithm combined with 3D convolution neural network has a good efficiency in identifying benign and malignant pulmonary nodules under different CT reconstruction classification  ...  The recursive neural network integrating attention mechanism is used to effectively utilize pulmonary nodules' global and local context information [15, 16] .  ... 
doi:10.1155/2022/3490463 pmid:35495882 pmcid:PMC9050279 fatcat:l2okiko2nrdarnc44unf5v3are
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