Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images

Qjidaa, M.; Ben-fares, A.; Mechbal, Y.; Amakdouf, Hicham; Maâroufi, Mustapha; Alami, Badreeddine; Qjidaa, Hassan

doi:10.1109/iscv49265.2020.9204282

Cited by 31 publications

(13 citation statements)

References 15 publications

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“…Training data was obtained from 701 COVID-19 patients through services within the network of Family Medical Clinics at New York University Langone Health Centers. A framework of clinical decision support for the early diagnosis of COVID 19 was suggested in (10) utilizing deep learning based on chest radiographic images. In order to have a medical assessment before the pathogen examination, they are designing an in-depth learning tool that could extract the graphical attributes of COVID-19.…”

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confidence: 99%

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Salah

Ahmed

2021

Baghdad Sci.J

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Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree.

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confidence: 99%

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Salah

Ahmed

2021

Baghdad Sci.J

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“…Most [29]. However, in the actual screening process, the penetration rate of X-rays will image the imaging of chest features.…”

Section: Discussionmentioning

confidence: 99%

MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

Pan

Wang

et al. 2021

Preprint

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The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

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“…Also tested on the chest X-ray image data set, the index exceeded the previous optimal model, and the results of detecting pneumonia were better than the level of professional radiologists. Qjidaa's team [53] applied the pre-trained and improved VGG16 network model to pneumonia prediction. The advantage of this network is that it is simple in design and can extract better features in the image.…”

Section: Pneumoniamentioning

confidence: 99%

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie¹,

Yu²,

Lv³

2021

Computer Modeling in Engineering &Amp; Sciences

162

104

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In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research.

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Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images

Cited by 31 publications

References 15 publications

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

Multi-Disease Prediction Based on Deep Learning: A Survey

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