During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal "Multimedia Tools and Applications" tops the list of journals with a total of 29 published articles.
Citation: Youness Chawki, Khalid Elasnaoui, Mohamed Ouhda. Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis[J]. AIMS Electronics and Electrical Engineering, 2024, 8(1): 71-103. doi: 10.3934/electreng.2024004
During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal "Multimedia Tools and Applications" tops the list of journals with a total of 29 published articles.
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Figures(29) / Tables(7)
Youness Chawki, Khalid Elasnaoui, Mohamed Ouhda. Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis[J]. AIMS Electronics and Electrical Engineering, 2024, 8(1): 71-103. doi: 10.3934/electreng.2024004
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