Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh, Rabab Ali; Nilashi, Mehrbakhsh; Ismail, Muhammed Yousoof; Alhargan, Ashwaq; Alghamdi, Abdullah; Alzahrani, Ahmed Omar; Saraireh, Linah; Osman, Reem; Asadi, Shahla

doi:10.1016/j.jiph.2021.11.013

Cited by 33 publications

(12 citation statements)

References 136 publications

(168 reference statements)

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“…Since the discovery of SARS-CoV-2, bibliometric analysis has been widely used in many aspects of the COVID-19 epidemic, such as vaccines (12,13), AI technology (14), and public health surveillance systems (15). Similarly, it is widely used in clinical departments, such as rheumatology (16), medical imaging (17), diabetes (18), orthopedics (19), and urology (20). Although there are many reported studies about PPE and COVID-19, there is no study on the bibliometric analysis of these studies.…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of Personal Protective Equipment and COVID-19 Researches

Wang

et al. 2022

Front. Public Health

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COVID-19, which occurred at the end of December 2019, has evolved into a global public health threat and affects every aspect of human life. COVID-19's high infectivity and mortality prompted governments and the scientific community to respond quickly to the pandemic outbreak. The application of personal protective equipment (PPE) is of great significance in overcoming the epidemic situation. Since the discovery of severe acute respiratory coronavirus 2 (SARS-CoV-2), bibliometric analysis has been widely used in many aspects of the COVID-19 epidemic. Although there are many reported studies about PPE and COVID-19, there is no study on the bibliometric analysis of these studies. The citation can be used as an indicator of the scientific influence of an article in its field. The aim of this study was to track the research trends and latest hotspots of COVID-19 in PPE by means of bibliometrics and visualization maps.

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Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of Personal Protective Equipment and COVID-19 Researches

Wang

et al. 2022

Front. Public Health

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“…Limited field of the study and lack of proper database information [46] ML methods for processing the medical image in the context of the COVID-19 crisis eight electronic databases: Elsevier, IEEE, PubMed, Wiley Online Library, Springer, Summon, Google Scholar, and Taylor and Francis…”

Section: Refmentioning

confidence: 99%

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis

Band¹,

Ardabili²,

Yarahmadi³

et al. 2022

Preprint

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Early diagnosis, prioritization, screening, clustering and tracking of COVID-19 patients, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, to manage and deal with this epidemic. Strategies backed by artificial intelligence (AI) and the Internet of Things (IoT) have been undeniable to understand how the virus works and try to prevent it from spreading. Accordingly, the main aim of this survey article is to highlight the methods of ML, IoT and the integration of IoT and ML-based techniques in the applications related to COVID-19 from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach of following the disease spread. Most of the studies developed by ML-based techniques for handling COVID-19 based dataset provided performance criteria. The most popular performance criteria, is related to accuracy factor. It can be employed for comparing the ML-based methods with different datasets. According to the results, CNN with SVM classifier, Genetic CNN and pre-trained CNN followed by ResNet, provided highest accuracy values. On the other hand, the lowest accuracy was related to single CNN followed by XGboost and KNN methods.

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“…With the development of computer hardware and software, mobile devices, image processing, and machine learning [5][6][7][8][9], it is feasible and meaningful to design a fast and efficient method to automatically identify and classify plant species. Plant species can be classified and identified by different organs such as leaves, flowers, fruits, or whole plants [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Yuan¹,

Liu²,

Song³

et al. 2023

Phyton

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Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.

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Medical image processing and COVID-19: A literature review and bibliometric analysis

Cited by 33 publications

References 136 publications

A Bibliometric Analysis of Personal Protective Equipment and COVID-19 Researches

A Bibliometric Analysis of Personal Protective Equipment and COVID-19 Researches

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis

Research on Plant Species Identification Based on Improved Convolutional Neural Network

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