Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

Albahri, O. S.; Zaidan, A. A.; Albahri, A. S.; Zaidan, B. B.; Abdulkareem, Karrar Hameed; Al-qaysi, Z.T.; Aleesa, Ahmed Marwan; Chyad, M. A.; Alesa, R M; Kem, L C; Lakulu, Muhammad Modi; Ibrahim, Abdelhameed; Rashid, Nazre Abdul

doi:10.1016/j.jiph.2020.06.028

Cited by 242 publications

(215 citation statements)

References 98 publications

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“…The procedure of AHP method is represented by the following steps. 74,75,93 Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID- Table 3. Prioritization DM.…”

Section: Phase Ii: Setting Weights For Biological Laboratory Covid-19mentioning

confidence: 99%

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

Albahri

Al‐Obaidi

Zaidan

et al. 2020

Int. J. Info. Tech. Dec. Mak.

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Coronavirus disease (COVID-19) pandemic has a tremendous effect on people’s lives worldwide, and the number of infected patients increases daily. The healthcare sector is affected by a large number of patients with COVID-19, and a solution is urgently needed to avert the risk of deteriorating patients in terms of prioritizing patients based on their health conditions. Prioritization of patients with COVID-19 is a complex and multi-criteria decision-analysis (MCDA) problem due to (i) multiple biological laboratory examination criteria, (ii) criteria importance and (iii) trade-off amongst the criteria. This study presents a new multi-biological laboratory examination framework for prioritizing patients with COVID-19 on the basis of integrated MCDA methods. The experiment was conducted on the basis of three phases. In the first phase, patient datasets containing eight biological laboratory examination criteria for six patients with COVID-19 were derived and discussed. The outcome of this phase was used to propose a decision matrix on the basis of the intersection between “biological laboratory examination criteria” and “COVID-19 patients list”. In the second phase, the analytic hierarchy process (AHP) method was utilized to set the subjective weights for the biological laboratory examination criteria by respiratory experts. In the last phase, the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method was adopted to prioritize patients in the context of individual and group decision making (GDM). Results showed that (1) the integration of AHP–VIKOR method based on individual and GDM contexts was effective for solving prioritization problems for patients with COVID-19, and (2) the prioritization results of patients with COVID-19 showed no variation in the internal and external VIKOR GDM contexts. The proposed multi-biological laboratory examination framework can differentiate between the mild and serious or critical condition of patients with COVID-19 by prioritizing them based on integrated AHP–VIKOR methods. In conclusion, medical sectors can use the proposed framework to differentiate the health conditions of infected patients and to assign appropriate care with prompt and effective treatment.

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Section: Phase Ii: Setting Weights For Biological Laboratory Covid-19mentioning

confidence: 99%

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

Albahri

Al‐Obaidi

Zaidan

et al. 2020

Int. J. Info. Tech. Dec. Mak.

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“…Medical Image Interpretation. There are many reports of artificial intelligence used in COVID-19 in radiologic imaging of COVID-19 with the deployment of deep learning and convolutional neural network (CNN) of chest X-rays and CT or MRI lung images ( 4 )( 5 ). Deep learning architectures included visual geometry group (VGG)-16 or VGG-19, Resnet 50, Xception, DenseNet201, Inception_ResNet_V2 and Inception_V3 with data sources that ranged from Kaggle, GitHub, and various hospitals, especially from cities in China.…”

Section: Global Health Primer With Relevance To Artificial Intelligenmentioning

confidence: 99%

Artificial intelligence and COVID-19: Present state and future vision

Chang¹

2020

Intelligence-Based Medicine

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The COVID-19 pandemic has lead to catastrophic number of deaths and revealed that much work still remains with data and artificial intelligence. To fully comprehend the dynamics of a pandemic with relevance to artificial intelligence, a primer on global health concepts is first presented. Following this, various aspects of diagnosis and therapy and the relationship to artificial intelligence are presented along with a future projection of an ideal deployment of artificial intelligence in a pandemic. Final thoughts are made about lessons learned and what lies ahead.

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“…Taxonomy of AI research literature used to identify and recognize medical images of COVID-19 based on four techniques: binary classifications, multiple classifications, mixed multiple-class and binary classifications and hybrid multiple -class and hierarchical classifications, as shown in (Fig. 6), [92], [93].…”

Section: Based On Artificial Image Techniquesmentioning

confidence: 99%

An Analysis Review, Detection Coronavirus Disease 2019 (COVID-19) based on Biosensor Application

Taha¹,

Arsad²,

Mashhadany³

2020

Preprint

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The global spread of coronavirus disease (COVID -19) worldwide has had a significant effect on social and economic growth. The contamination keeps on advancing quickly and eccentrically, representing a significant test to its recognition and conclusion. Coronaviruses are commonly recognized by seclusion from tests, regardless of whether natural or clinical, utilizing some atomic science procedures, which can take a few days. In this work an analytical review of virus transmission, methods of diagnosing COVID -19 using artificial intelligence techniques to classify images and types of biosensors. At long last, the deformities and points of interest of each kind of sensor are recognized and examined. This exploration gives an explanatory audit of the utilization of crown infection COVID-19 in 2019. Related examinations were led utilizing five dependable databases, for example, Science Direct, IEEE Xplore, Scopus, Web of Science, and PubMed. An acceptable investigation is remembered for this audit, which can be depended upon as a logical database to put resources into another technique for recognizing COIVD-19.

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Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

Cited by 242 publications

References 98 publications

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

Artificial intelligence and COVID-19: Present state and future vision

An Analysis Review, Detection Coronavirus Disease 2019 (COVID-19) based on Biosensor Application

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