A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images

Rasheed, Jawad; Hameed, Alaa Ali; Djeddi, Chawki; Jamıl, Akhtar; Al‐Turjman, Fadi

doi:10.1007/s12539-020-00403-6

Cited by 166 publications

(89 citation statements)

References 44 publications

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“…In Kikkisetti et al (2020), to classify the lungs infected images the chest X-ray (CXR) and deep-learning CNN are applied. In Rasheed et al (2020), CNN models and the logistic regression (LR) are used to classify CXR images. In Rahimzadeh and Attar (2020), for an unbalanced data set a neural network is used to detect COVID-19 cases.…”

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RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models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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“…(5) The final decision function of SVM is determined by a small number of support vectors, and the computational complexity is determined by the number of support vectors, which undoubtedly does not avoid the dimensionality disaster. (6) SVM can capture key samples and eliminate a large number of redundant samples, and the method is simple and has good robustness [5,30].…”

Section: Support Vector Machines Have the Following Characteristicsmentioning

confidence: 99%

“…Sensor technology, data information processing technology, and wireless communication technology have been increasingly used in medical equipment. At present, researchers are also beginning to apply these new technologies to the field of rehabilitation training and evaluation and to study a new type of physiological parameter monitoring system for rehabilitation training to solve the problems of the traditional methods mentioned above [5]. For example, WBAN can measure a person's important physiological signals and issue a warning before the patient's heart disease has been affected.…”

Section: Introductionmentioning

confidence: 99%

The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19

Tao

et al. 2021

Pers Ubiquit Comput

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Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients' lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.

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“…Among them, 51~75% was 3 points, and > 75% was 4 points. According to the score, we can more directly reflect the involvement of lesions [12,13].…”

Section: Application Methods Of Ct Detection Technology In Covid-19 DImentioning

confidence: 99%

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

Zhang

Liu

Shao

et al. 2021

Pers Ubiquit Comput

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The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence-assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence-assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

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A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images

Cited by 166 publications

References 44 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

The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

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