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Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier [article]

Yuxing Tang and Youbao Tang and Mei Han and Jing Xiao and Ronald M. Summers
2019 arXiv   pre-print
In this paper, we propose an end-to-end architecture for abnormal chest X-ray identification using generative adversarial one-class learning.  ...  It thus enables distinguishing abnormal chest X-rays from normal ones.  ...  ACKNOWLEDGMENTS Fig. 1 . 1 Framework of the proposed deep adversarial one-class learning model for abnormal chest X-ray identification.  ... 
arXiv:1903.02040v1 fatcat:fwlvd6hrqfg43mvfh2p4aznqgy

A Survey on Deep Convolutional Generative Adversarial Neural Network (DCGAN) for Detection of Covid-19 using Chest X-ray/CT-Scan

S. Nikkath Bushra, G. Shobana
2020 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS)  
This paper aims to overview the details about the systems recently developed to diagnose novel COVID-19 with the help of X-ray and CT-scan images collected from different infected persons using one of  ...  Keywords-Deep Convolutional Generative Adversarial Network (DCGAN), Computerized Tomography (CT), Reverse Transcriptase-polymerase chain reaction (RT-PCR), Chest Xrays (CXR) I.  ...  VGG-19 classifier can predict 46 out of 68 covid images accurately with the baseline generated X-rays whereas VGG-19 predicts 63 of the 68 X-rays accurately using the X-rays generated by MTT-GAN which  ... 
doi:10.1109/iciss49785.2020.9316125 fatcat:vpy3kfz45jdp5lalnlk6mhh6y4

Systematic Study on Diagnosis of Lung Disorders using Machine Learning and Deep Learning Algorithms

R. Swathi Sri, A. Menaka Pushpa
2021 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)  
Deep learning models work on medical images to detect the type of lung disease.  ...  Presently, lung infection is severe to humans that leads to death if left untreated, and Tracking down a disease on the dot is a way we get out of a hock.  ...  Generative Adversarial Networks (GAN): The lung image segmentation is performed by generative adversarial networks on the chest X-Ray image.  ... 
doi:10.1109/icbsii51839.2021.9445186 fatcat:wzitd6euyzg77efdfathektzli

Bias Field Poses a Threat to DNN-based X-Ray Recognition [article]

Binyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, Shengchao Qin
2021 arXiv   pre-print
We validate our method on real chest X-ray datasets with powerful DNNs, e.g., ResNet50, DenseNet121, and MobileNet, and show different properties to the state-of-the-art attacks in both image realisticity  ...  The chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19.  ...  , chest X-ray and dermoscopy respectively.  ... 
arXiv:2009.09247v2 fatcat:pse6nppprvh2pmonqgz3aa76ze

Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays

Shehroz S. Khan, Faraz Khoshbakhtian, Ahmed Bilal Ashraf
2021 Proceedings of the Canadian Conference on Artificial Intelligence  
We tested two settings on a publicly available dataset (COVIDx) by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID  ...  These results are very encouraging and pave the way towards research for ensuring emergency preparedness in future pandemics, especially the ones that could be detected from chest X-rays.  ...  On the other hand, Chest X-ray (CXR) is a promising imaging modality as it is easily available, and can be used for rapid triaging [12] .  ... 
doi:10.21428/594757db.fab70f8a fatcat:huceiiodbfexrint663kx7qmne

Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays [article]

Shehroz S. Khan, Faraz Khoshbakhtian, Ahmed Bilal Ashraf
2021 arXiv   pre-print
We tested two settings on a publicly available dataset (COVIDx)by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID-  ...  These results are very encouraging and pave the way towards research for ensuring emergency preparedness in future pandemics, especially the ones that could be detected from chest X-rays  ...  On the other hand, Chest X-ray (CXR) is a promising imaging modality as it is easily available, and can be used for rapid triaging [12] .  ... 
arXiv:2010.02814v2 fatcat:vwumcbf7qjf2xjva34stoeprlm

Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review [article]

Wasif Khan, Nazar Zaki, Luqman Ali
2020 medRxiv   pre-print
This paper overviews the current literature on pneumonia identification from chest x-ray images.  ...  Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review  ...  pneumonia • Chest radiography OR Chest x-rays OR Chest Disease detection • data balancing AND chest X-rays OR pneumonia, Generative adversarial networks (GANs) AND chest X-rays • Search string for the  ... 
doi:10.1101/2020.07.09.20150342 fatcat:kuhmml67x5erfgtxf2kpq6fnyu

Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets [article]

Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu, Alexandros Karargyris, Mehdi Moradi
2020 arXiv   pre-print
Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.  ...  Subsequently, several datasets and deep learning-based solutions have been proposed to identify diseases based on chest X-ray images.  ...  The interpretation of activation maps in chest X-ray images is generally challenging.  ... 
arXiv:2008.04152v1 fatcat:ex2mcfpogjf2hjh4wxalw53hly

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

Saleh Albahli, Tahira Nazir
2022 Frontiers in Medicine  
high inter-class similarities, and intra-class variations in abnormalities.  ...  Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities.  ...  One of such applications is chest X-ray (CXR) analysis.  ... 
doi:10.3389/fmed.2022.955765 pmid:36111113 pmcid:PMC9469020 fatcat:fqceqyrn5vhrbn7r6qd4zq3vai

COVID-19 Data Analysis using Chest X-ray

Ishtiaque Ahmed, Student, SVKM's Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India., Manan Darda, Neha Tikyani, Rachit Agrawal, Dr. Manjusha Joshi, Student, SVKM's Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India., Student, SVKM's Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India., Student, SVKM's Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India., Assistant Professor, SVKM's Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.
2021 International Journal of Advanced Medical Sciences and Technology  
Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities.  ...  To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources.  ...  For the model's training, a set of 3000 chest X-ray images was classified into three classes: "COVID-19', 'normal' and 'viral pneumonia'.  ... 
doi:10.35940/ijamst.c3018.081421 fatcat:cwtlszx66vaslmrox3zgzy7a3y

Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

Joana Rocha, Ana Maria Mendonça, Aurélio Campilho
2021 U Porto Journal of Engineering  
Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality  ...  This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison.  ...  Tang et al. (2019) adopt this research line and suggest an end-to-end architecture for abnormality detection using generative adversarial one-class learning and ChestX-ray14 (Figure 2 ).  ... 
doi:10.24840/2183-6493_007.004_0002 fatcat:iwtaykty7jcydjk67sdg5gad7a

Modify Convolutional Neural Network Model for The Diagnosis of Multi-classes Lung Diseases Covid-19 And Pneumonia Based on X-ray Images

Omer Kareem, University of Duhok, Ahmed Al-sulaifanie, University of Duhok
2022 The Journal of The University of Duhok  
According to numerous studies, visual markers (abnormalities) on a patient's Chest X-Ray imaging can be a valuable characteristic of a COVID-19 patient, which can be exploited to discover the virus.  ...  In this research, Convolutional Neural Networks (CNNs) are being proposed to detect the Covid-19 disease based on X-rays images.  ...  There are many ways to identify abnormalities in standard chest X-ray images.  ... 
doi:10.26682/sjuod.2022.25.1.9 fatcat:l762nj6pazdupdbo4b225ohx34

Bias Field Poses a Threat to DNN-Based X-Ray Recognition

Binyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, Shengchao Qin
2021 2021 IEEE International Conference on Multimedia and Expo (ICME)  
We validate our method on real chest X-ray datasets with powerful DNNs, e.g., ResNet50, DenseNet121, and MobileNet, and show different properties to the state-of-the-art attacks in both image realisticity  ...  Chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19.  ...  [21] shows that both black box and white box PGD attack and adversarial patch attack can affect the classifiers' performance on fundoscopy, chest X-ray and dermoscopy, respectively.  ... 
doi:10.1109/icme51207.2021.9428437 fatcat:4cvutmx4abcf3kuuqfn47dzbzm

Utilizing Knowledge Distillation in Deep Learning for Classification of Chest X-ray Abnormalities

Thi Kieu Khanh Ho, Jeonghwan Gwak
2020 IEEE Access  
[37] employed deep convolutional generative adversarial networks (DCGAN) to generate artificial images from five common pathological classes, then applied it to chest X-rays.  ...  of 14 abnormalities appearing in chest X-rays, along with saliency map visualizations to ensure the accurate identification of abnormal regions.  ... 
doi:10.1109/access.2020.3020802 fatcat:ucyyrx3ijzft3he2kabn7tgqyi

Automatic Diagnosis of Pneumothorax from Chest Radiographs: A Systematic Literature Review [article]

Tahira Iqbal, Arslan Shaukat, Usman Akram, Zartasha Mustansar
2021 arXiv   pre-print
This study summarizes the existing literature for the automatic detection of pneumothorax from chest x-rays along with describing the available chest radiographs datasets.  ...  Lot of research has been done for automatic and fast detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence and machine learning techniques  ...  Diagnosis of chest pathologies OR pneumothorax from Chest radiographs iii. Chest X-rays datasets OR datasets for pneumothorax identification iv.  ... 
arXiv:2012.11214v2 fatcat:c2oa374cvrgvteysabzokvn3ba
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