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Lung Nodule Classification using Deep Local-Global Networks
[article]
2019
arXiv
pre-print
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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
2018
Complexity
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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]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.14117v1 fatcat:y3es7cwdkneovkhmlebbhpxztmOther Versions
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
2020
Computational Intelligence and Neuroscience
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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
2021
BMC Medical Imaging
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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
2020
Sensors
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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]
2018
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2018
BioMedical Engineering OnLine
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[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
2022
Diagnostics
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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
2018
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
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2018 pre-print arXiv:1803.07192v1 fatcat:esrvcs2j7fbr7byqnmf3y53k2q
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
2020
IEEE Access
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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]
2021
arXiv
pre-print
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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
2022
Journal of Digital Health
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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
2022
Computational and Mathematical Methods in Medicine
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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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