COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

Makris, Antonios; Kontopoulos, Ioannis; Tserpes, Konstantinos

doi:10.1145/3411408.3411416

Cited by 103 publications

(46 citation statements)

References 8 publications

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“…In Lozano et al (2020), information to predict a fatal outcome in patients with COVID-19 is provided using an ANN. 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.…”

Section: Literature Survey On Mathematical Modelsmentioning

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RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models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

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 contrast to these, many researchers relied on transfer learning approach for detection of COVID cases. Markis et al [19] performed a large number of experiments (38 in total) to establish the necessity of using transfer learning for the current problem for both deep learners and shallow learners. In another work, Panwar et al [20] first used pre-trained VGG19 CNN model to classify CXR images.…”

Section: Literature Surveymentioning

confidence: 99%

Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images

Das

Roy

Malakar³

et al. 2021

Big Data Research

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“…Recently, deep learning has become a preferred technique for analyzing Xrays [9], [10], and CT scans [11], [12] and is being applied for both classification and segmentation tasks. Makris [13] used transfer learning technique to make a comparative study between different convolutional neural network pre-trained models: VGG16 [14] , VGG19 [14], MobileNetV2 [15], InceptionV3 [16], Xception [17], InceptionResNetV2 [18], DenseNet201 [19] , ResNet152V2 [20], NASNetLarge [21]. A combination of two publicly available datasets was used in [10], [11]; the first one contains 112 Posterior-Anterior XRay scans for COVID-19 confirmed cases, the second consists of 112 healthy scans (normal cases) and 112 scans for common bacterial Pneumonia.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 detection from Xray and CT scans using transfer learning

Berrimi

Hamdi

Cherif

et al. 2021

2021 International Conference of Women in Data Science at Taif University (WiDSTaif )

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results than PCR [4]. The researchers also mentioned that Compared to RT-PCR, chest CT imaging might be a more reliable, practical and a rapid method to diagnose and assess COVID-19, especially in the epidemic area.Although many centers recommend the use of CT scans on Xrays, research [5] has revealed that Xrays can effectively predict which patients are more likely to develop their symptoms, especially for people aged 21-50, therefore early detection of COVID-19 using Xrays scans is very critic, and can help quickly isolating the infected people, as presented in "Fig. III-B", where the virus damages occurs differs day by day in the patient's lungs. It is also noted that SARS-CoV-2 Pneumonia should be initially distinguished from other pneumonia diseases since they present quite similar scans in some cases. Therefore, it is important to establish an automated tool that helps doctors in detecting COVID-19 on chest scans. Deep learning, in particular convolutional networks, has demonstrated state-of-art achievements in medical imaging analysis in many application areas, such as neurological disorders, retinal diseases analysis [6], pulmonary infections, digital pathology, breast anatomy imaging, cardiology, abdominal treatment, and musculoskeletal disorders [7]. Typically, convolutional networks perform better on large scale datasets; therefore, in case of small scale dataset, one way to overcome this deficiency is to use transfer learning. In the latter, a model is trained on one task (where a large scale dataset is available) is fine-tuned on the second task (where only a small sale dataset is available). Most traditional learning methods build and train new baselines and classifiers from scratch for each classification task [8]. On the other hand, Transfer learning re-use and transfer knowledge learnt from a source classifier that have been trained on large scale databases to simplify the construction of a classifier for a new target Abstract-Since the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find s uitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role i n this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods.

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COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

Cited by 103 publications

References 8 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

Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images

COVID-19 detection from Xray and CT scans using transfer learning

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