Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

Şekeroğlu, Boran; Ozsahin, Ilker

doi:10.1177/2472630320958376

Cited by 134 publications

(91 citation statements)

References 25 publications

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“…In Makris et al (2020), to detect infected patients from CXR images CNNs are used. In Sekeroglu and Ozsahin (2020), by the training of deep learning and machine learning classifiers detected COVID-19 patients from their CXR images. In , a deep CNN is to identify the infection of COVID-19 from the CXR image of the lungs of the patients to save the medical doctors time in diagnosis.…”

Section: Literature Survey On Mathematical Modelsmentioning

confidence: 99%

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RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202–207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature.

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Section: Literature Survey On Mathematical Modelsmentioning

confidence: 99%

“…( 2020 ), to detect infected patients from CXR images CNNs are used. In Sekeroglu and Ozsahin ( 2020 ), by the training of deep learning and machine learning classifiers detected COVID-19 patients from their CXR images. In Singh et al.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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“…In this work, we verified experimental results using various evaluation metrics like accuracy, AUC, F-measure, sensitivity, and specificity respectively. Several works have analyzed a few number of COVID-19 samples along with other cases where the experimental dataset was remained imbalanced (Karar et al, 2020; Sekeroglu & Ozsahin, 2020; Shankar & Perumal, 2020; Zebin & Rezvy, 2020). Sometimes, they were conducted with a separate dataset where the scarcity of samples was found in both of these datasets (Apostolopoulos & Mpesiana, 2020; Shankar & Perumal, 2020).…”

Section: Experiments Resultsmentioning

confidence: 99%

Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-ray Images

Satu¹,

Ahammed

Abedin

et al. 2020

Preprint

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This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy (94.03%), AUC (95.52%), f-measure (94.03%), sensitivity (94.03%) and specificity (97.01%) as well as the lowest fall out (4.48%) and miss rate (2.98%) respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.

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“…In addition, the transfer learning method is used to train four CNNs, including ResNet18, ResNet50, SqueezeNet and DenseNet-121, to identify COVID-19 symptoms in the analyzed chest X-ray images, and three of these networks do not exceed a sensitivity rate of 98%, while the results of the other one are not considerable at all [55]. The VGG-19 and the MobileNet-V2 are employed by the authors of [56] and they confirm that these two networks are not capable of classifying the COVID-19 X-ray images. The ResNet-50 and VGG-16 produce comparatively better results than VGG-19 and MobileNet-V2.…”

Section: Related Workmentioning

confidence: 99%

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

et al. 2021

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In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%.

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Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

Cited by 134 publications

References 25 publications

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-ray Images

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

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