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COVID-19 Diagnostic System Using Medical Image Classification and Retrieval: A Novel Method for Image Analysis

Maher Alrahhal, Supreethi K P
2021 Computer journal  
The performance of the classification task in terms of accuracy was as follows: 100% for classifying the input image to X-ray or CT scan, 99.18% for classifying X-ray image to COVID-19 or NOTCOVID-19 and  ...  based on Bag of Visual Word methodology.  ...  images Classes label No. of images in each class 1 Used to classify images into X-ray or CT scan. 2 600 X-ray 300 CT 300 2 Used for first level of X-ray classification. 2 1225 COVID  ... 
doi:10.1093/comjnl/bxab051 fatcat:sd66njlwcrgond3ybw4kda5g7i

New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis

Chiranjibi Sitaula, Sunil Aryal
2021 Health Information Science and Systems  
Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the Pneumonia-like effect in the lung, the examination of Chest X-Rays (CXR) can help diagnose the disease.  ...  We evaluate the effectiveness of our proposed BoDVW features in CXR image classification using Support Vector Machine (SVM) to diagnose COVID-19.  ...  Sunil Aryal is supported by the joint grant by US Air Force Office of Scientific Research and Office of Naval Research under award number FA2386-20-1-4005.  ... 
doi:10.1007/s13755-021-00152-w pmid:34164119 pmcid:PMC8213041 fatcat:gvznwr3iwndtxk2xk6dtpj25se

An automated and fast system to identify COVID-19 from X-ray radiograph of the chest using image processing and machine learning

Murtaza Ali Khan
2021 International journal of imaging systems and technology (Print)  
During testing, an X-ray radiograph's visual vocabulary is sent to the trained SVM classifier to detect the absence or presence of COVID-19.  ...  This article presents an automated and fast system that identifies COVID-19 from X-ray radiographs of the chest using image processing and machine learning algorithms.  ...  The center of a cluster is called the visual word. The bag of visual-words consists of all the visual-words. Next, the bag of visual-words is the pass to the SVM classifier for training.  ... 
doi:10.1002/ima.22564 pmid:33821097 pmcid:PMC8014629 fatcat:ogtgt2ko55bi5p3cyf5q66epbi

A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of COVID 19 Through X Rays Images

Jalal RABBAH, Mohammed RIDOUANI, Larbi HASSOUNI
2020 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS)  
extraction from X-Ray images.  ...  The use of machine learning models constitutes a new approach, used more and more in this field.  ...  pre-trained Deep Convolutional Neural Network, used for features extraction from X-Ray images.  ... 
doi:10.1109/icds50568.2020.9268777 fatcat:mjj7wmdskjbnnjqe5pyutfaj4q

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID-19 Detection

Amin Valizadeh, Morteza Shariatee, Suresh Manic
2021 Computational Intelligence and Neuroscience  
Finally, a general conclusion on the use of developed advances based on deep neural network concepts in semantic segmentation is presented.  ...  In this study, the method of semantic segmenting images is split into two sections: the method of the deep neural network and previous traditional method.  ...  Acknowledgments e funding sources had no involvement support in the study design, collection, analysis, or interpretation of data, in writing of the manuscript, or in the decision to submit the manuscript  ... 
doi:10.1155/2021/7265644 pmid:34840563 pmcid:PMC8611358 fatcat:6xsjrgiabjat5c6uinwa2kbyay

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

S. V. Kogilavani, J. Prabhu, R. Sandhiya, M. Sandeep Kumar, UmaShankar Subramaniam, Alagar Karthick, M. Muhibbullah, Sharmila Banu Sheik Imam, Muhammad Zubair Asghar
2022 Computational and Mathematical Methods in Medicine  
In 2019, the city of Wuhan reported the first-ever incidence of COVID-19.  ...  To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality.  ...  The SURF method is utilized to extract objects in a visual word bag.  ... 
doi:10.1155/2022/7672196 pmid:35116074 pmcid:PMC8805449 fatcat:uzr646xjsfa5pfrg2y2kg6k3wu

A Survey on Machine Learning in COVID-19 Diagnosis

Xing Guo, Yu-Dong Zhang, Siyuan Lu, Zhihai Lu
2022 CMES - Computer Modeling in Engineering & Sciences  
Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems.  ...  neural networks, and so on.  ...  They were trained and tested in the COVID-19 X-ray dataset and achieved a classification accuracy of 99.18. In addition, K-NN is commonly used in image classification.  ... 
doi:10.32604/cmes.2022.017679 fatcat:hre5zxtekvaevleu335faqilwu

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

Stefanus Tao Hwa Kieu, Abdullah Bade, Mohd Hanafi Ahmad Hijazi, Hoshang Kolivand
2020 Journal of Imaging  
Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images.  ...  The recent developments of deep learning support the identification and classification of lung diseases in medical images.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/jimaging6120131 pmid:34460528 fatcat:jhi5r4nj5nccbklrdbtuk4qo6e

Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation

Hasib Zunair, A. Ben Hamza
2021 Social Network Analysis and Mining  
chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training.  ...  In addition, the proposed data generation framework offers a viable solution to the COVID-19 detection in particular, and to medical image classification tasks in general.  ...  Acknowledgements This work was supported in part by and Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Number N00929.  ... 
doi:10.1007/s13278-021-00731-5 pmid:33643491 pmcid:PMC7903408 fatcat:d2dple2v75e2pobmfqarwo2a5m

Classification of COVID-19 and Pneumonia Using Deep Transfer Learning

Mainuzzaman Mahin, Sajid Tonmoy, Rufaed Islam, Tahia Tazin, Mohammad Monirujjaman Khan, Sami Bourouis, Kalidoss Rajakani
2021 Journal of Healthcare Engineering  
According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images.  ...  Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent.  ...  In the quickest way possible, these models can detect COVID-19 and pneumonia using a simple chest X-ray image. X-ray technology is now available and is also cost-effective.  ... 
doi:10.1155/2021/3514821 pmid:34956569 pmcid:PMC8702319 fatcat:onzqcmi4fnbrvbcr45ealslski

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images [article]

Qinghao Ye, Yuan Gao, Weiping Ding, Zhangming Niu, Chengjia Wang, Yinghui Jiang, Minhao Wang, Evandro Fei Fang, Wade Menpes-Smith, Jun Xia, Guang Yang
2021 arXiv   pre-print
Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers.  ...  The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2  ...  Acharya, Automated detection of covid-19 cases using deep neural networks with x-ray images, Computers in Biology and Medicine (2020) 103792. [34] Y. Oh, S. Park, J. C.  ... 
arXiv:2112.04984v1 fatcat:spnk3ztuevcavgaje6acjp4ula

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

Mohammad-H. Tayarani-N.
2020 Chaos, Solitons & Fractals  
a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc.  ...  Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country  ...  A new convolutional CapsNet is proposed in [286] for the detection of covid-19 cases by using X-Ray images with capsule network. In another work [287] , the same problem is targeted.  ... 
doi:10.1016/j.chaos.2020.110338 pmid:33041533 pmcid:PMC7532790 fatcat:gl3i37hag5gflajsa7fh6khvva

New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images

Ahmad Mozaffer Karim, Hilal Kaya, Veysel Alcan, Baha Sen, Ismail Alihan Hadimlioglu
2022 Symmetry  
A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass  ...  In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques.  ...  In another study, authors proposed a deep neural network-based method nCOVnet, an alternative on fast screening to detect the COVID-19 by analyzing the X-rays of patients [23] . Zebin et al.  ... 
doi:10.3390/sym14051003 fatcat:lukxjk25ofhdni5edemvdgyh4u

TMRGM: A Template-Based Multi-Attention Model for X-Ray Imaging Report Generation

Xuwen Wang, Yu Zhang, Zhen Guo, Jiao Li
2021 Journal of Artificial Intelligence for Medical Sciences  
This paper aims to extract valuable information automatically from medical images to assist doctors in chest X-ray image interpretation.  ...  In this study, we developed an experimental dataset based on the IU X-ray collection to validate the effectiveness of TMRGM model.  ...  ACKNOWLEDGMENTS This work has been supported by the National Natural Science  ... 
doi:10.2991/jaims.d.210428.002 fatcat:tkp2o55xk5h4rcmfr4b3enpuou

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

Swarnava Biswas, Debajit Sen, Dinesh Bhatia, Moumita Mukherjee, Department of Physics, School of Basic and Applied Sciences, Adamas University, Kolkata, West Bengal, India
2021 International Journal of Statistics in Medical Research  
Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent  ...  Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating  ...  Pretrained convolutional neural networks were tested for their ability to detect infection in chest X-ray pictures.  ... 
doi:10.6000/1929-6029.2021.10.14 fatcat:42ewzz2v5zekzgu3nwrog3d6mu
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