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X-ray image based pneumonia classification using convolutional neural networks

Sarah Badr AlSumairi, Mohamed Maher Ben Ismail
2020 ACCENTS Transactions on Image Processing and Computer Vision  
Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia.  ...  In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem.  ...  Conflicts of interest The authors have no conflicts of interest to declare.  ... 
doi:10.19101/tipcv.2020.618050 fatcat:iv364mljmbedtmagnhjslnby2u

Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network

Sohaib Asif, Yi Wenhui, Hou Jin, Si Jinhai
2020 2020 IEEE 6th International Conference on Computer and Communications (ICCC)  
In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification  ...  This study aimed to automatically detect COVID--19 pneumonia patients using digital chest x-ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN).  ...  In this study, we proposed a deep transfer learning-based approach the use of chest X-ray images obtained from COVID-19 patients, normal and viral pneumonia for automatic detection of COVID-19 pneumonia  ... 
doi:10.1109/iccc51575.2020.9344870 fatcat:tl4qqlklpzfxlnj5jphldzqqee

Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning [article]

Sohaib Asif, Yi Wenhui
2020 medRxiv   pre-print
In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and achieved more than  ...  The dataset consists of 864 COVID‐19, 1345 viral pneumonia and 1341 normal chest xray images.  ...  In this study, we proposed a deep transfer learning-based approach the use of chest X-ray images obtained from COVID-19 patients, normal and viral pneumonia for automatic detection of COVID-19 pneumonia  ... 
doi:10.1101/2020.05.01.20088211 fatcat:3fvifflv4bg35omuhy7rgoxbka

A Research Agenda on Pediatric Chest X-Ray: Is Deep Learning Still in Childhood? [article]

Afonso U. Fonseca, Gabriel S. Vieira, Fabrízzio A. A. M. N. Soares, Renato F. Bulcão-Neto
2020 arXiv   pre-print
to pediatric chest X-rays (PCXR).  ...  Several reasons explain the significant role that chest X-rays play on supporting clinical analysis and early disease detection in pediatric patients, such as low cost, high resolution, low radiation levels  ...  ACKNOWLEDGMENTS We thank all those who collaborate directly and indirectly for the execution of this study, in particular to the deep learning specialists of the group Deep Learning Brazil who collaborated  ... 
arXiv:2007.11369v2 fatcat:wli6e6fba5fodix57jbnosk7l4

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images

Fareed Ahmad, Amjad Farooq, Muhammad Usman Ghani, Luis Javier Herrera
2021 Computational Intelligence and Neuroscience  
Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections.  ...  Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays.  ...  In [62] , a deep learning model based on Compressed Sensing for computer-aided disease detection on chest X-ray images was suggested to support the doctors.  ... 
doi:10.1155/2021/8890226 pmid:33488691 pmcid:PMC7805527 fatcat:zx6quzy4kranronmz43brb3q4m

Detection of COVID-19 and Other Pneumonia Cases Using Convolutional Neural Networks and X-ray Images

Carlos Eduardo Belman López
2021 Ingeniería e Investigación  
The contribution of this paper is to present new models for detecting COVID-19 and other cases of pneumonia using chest X-ray images and convolutional neural networks, thus providing accurate diagnostics  ...  Given that it is fundamental to detect positive COVID-19 cases and treat affected patients quickly to mitigate the impact of the virus, X-ray images have been subjected to research regarding COVID-19,  ...  Acknowledgements The author would like to thank the Mexican Council of Science and Technology (CONACYT -Consejo Nacional de Ciencia y Tecnología) for financing this research by awarding a scholarship for  ... 
doi:10.15446/ing.investig.v42n1.90289 fatcat:inoriao5d5cb3fihmupjcx3tvi

Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment

Mohamed Ramzy Ibrahim, Sherin M. Youssef, Karma M. Fathalla
2021 Journal of Ambient Intelligence and Humanized Computing  
COV-CAF fuses traditional and deep learning approaches. The proposed COV-CAF consists of two phases: the preparatory phase and the feature analysis and classification phase.  ...  The infection with SARS-COV-2 virus shows an abnormality in lung parenchyma that can be effectively detected using Computed Tomography (CT) imaging.  ...  Thus, no confident diagnosis of COVID-19 disease is possible based on chest X-ray alone (Rony Kampalath; Zu et al. 2020) .  ... 
doi:10.1007/s12652-021-03282-x pmid:34055098 pmcid:PMC8147594 fatcat:dx637yujpzfpnppiaoxqmgocei

DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images [article]

Md. Rezaul Karim, Till Döhmen, Dietrich Rebholz-Schuhmann, Stefan Decker, Michael Cochez, Oya Beyan
2020 arXiv   pre-print
In this paper, we propose an explainable deep neural networks(DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call DeepCOVIDExplainer.  ...  One methodology is the assessment of chest radiography(CXR) images, which usually requires expert radiologist's knowledge.  ...  [24] , a deep learning model called DarkCovidNet is proposed for the automatic diagnosis of COVID-19 based on CXR images.  ... 
arXiv:2004.04582v3 fatcat:cix4mbfimjd2hgpoklp3nkhth4

Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19

Plácido L Vidal, Joaquim Moura, Jorge Novo, Marcos Ortega
2021 Expert systems with applications  
This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays.  ...  lung regions from portable X-ray devices despite the scarcity of samples and lesser quality.  ...  Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper  ... 
doi:10.1016/j.eswa.2021.114677 pmid:33612998 pmcid:PMC7879025 fatcat:eyrkvgjd4vddnf2yph7i5zehee

AIoT Used for COVID-19 Pandemic Prevention and Control

Shu-Wen Chen, Xiao-Wei Gu, Jia-Ji Wang, Hui-Sheng Zhu, Yu-Dong Zhang
2021 Contrast Media & Molecular Imaging  
For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped  ...  Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is  ...  Singh and Singh [28] 6,500 chest X-rays e overall accuracy is 95.83%. It is used to diagnose COVID-19 from chest X-ray images. Sivaramakrishnan et al.  ... 
doi:10.1155/2021/3257035 pmid:34729056 pmcid:PMC8514960 fatcat:rknhbjoasjbn5ivhrob3udvuhq

Deep Learning-Enabled Technologies for Bioimage Analysis

Fazle Rabbi, Sajjad Rahmani Dabbagh, Pelin Angin, Ali Kemal Yetisen, Savas Tasoglu
2022 Micromachines  
Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical  ...  testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.  ...  In another study, chest X-rays images were classified using a deep two-class classification method, yielding a classification accuracy of 96.34% (between COVID-19 and bacterial pneumonia chest X-rays)  ... 
doi:10.3390/mi13020260 pmid:35208385 pmcid:PMC8880650 fatcat:xbem7lix4nhm7cbaauye46lnye

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Luis Bote-Curiel, Sergio Muñoz-Romero, Alicia Gerrero-Curieses, José Luis Rojo-Álvarez
2019 Applied Sciences  
Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning.  ...  Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9112331 fatcat:3szr5juksvhafnn2odfkmvrqni

Artificial Intellgence – Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 [article]

Karl-Herbert Schäfer
2021 arXiv   pre-print
, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture  ...  and management) and the University of Applied Sciences and Arts Northwestern Switzerland.  ...  Wetzel and T. Iraki for helpful comments and discussion.  ... 
arXiv:2112.05657v1 fatcat:wdjgymicyrfybg5zth2dc2i3ni

A deep look into radiomics

Camilla Scapicchio, Michela Gabelloni, Andrea Barucci, Dania Cioni, Luca Saba, Emanuele Neri
2021 Radiologia Medica  
, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation.  ...  in cancer detection, diagnosis, prognosis and treatment evaluation.  ...  The manuscript is supported by the Master Course in Oncologic Imaging of the Department of Translational Research, University of Pisa.  ... 
doi:10.1007/s11547-021-01389-x pmid:34213702 pmcid:PMC8520512 fatcat:gegnlv42zjfqpila7fa56jyeyy

Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death

Ayan Chatterjee, Martin W. Gerdes, Santiago G. Martinez
2020 Sensors  
and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths.  ...  For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three  ...  [32] and Wang et al. [33] did their research on Deep Convolutional Neural Network Design to identify the COVID-19 cases from the chest X-ray images. Ghosal et al.  ... 
doi:10.3390/s20113089 pmid:32486055 pmcid:PMC7308840 fatcat:k2xfjlat3jghpluhpn2xcew6wi
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