An intelligent deep convolutional network based COVID-19 detection from chest X-rays

Alshahrni, Mohammad M.; Ahmad, Mostafa A.; Abdullah, M.K.; Omer, Nadir; Azziz, Muzzamil

doi:10.1016/j.aej.2022.09.016

Cited by 8 publications

(1 citation statement)

References 61 publications

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“…The ensemble learning approach is also applied to management innovation, i.e., policymaking [64], socio-economic factor analysis [65], and green business management innovation [66]. In biology and medicine, i.e., predict metal oxide nanoparticle toxicity in immune cells [67] and detection for COVID-19 [68]. In the aspect of nanocomposite materials, Gradient Boosted Trees is applied [69].…”

Section: Application In M Categorymentioning

confidence: 99%

Ensemble Learning Applications in Multiple Industries: A Review

Lin¹,

Huang²

2023

IDA

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This study proposes a systematic review of the application of Ensemble learning (EL) in multiple industries. This study aims to review prevailing application in multiple industries to guide for the future landing application. This study also proposes a research method based on Systematic Literature Review (SLR) to address EL literature and help advance our understanding of EL for future optimization. The literature is divided three categories by the National Bureau of Statistics of China (NBSC): the primary industry, the secondary industry and the tertiary industry. Among existing problems in industrial management systems, the frequently discussed are quality control, prediction, detection, efficiency and satisfaction. In addition, given the huge potential in various fields, the gap and further directions are also suggested. This study is essential to industry managers and cross-disciplinary scholars to lead a guideline to solve the issues in practical work, as it provided a panorama of application domains and current problems. This is the first review of the application of EL in multiple industries in the literature. The paper has potential values to broaden the application area of EL, and to proposed a novel research method based SLR to sort out literature.

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Section: Application In M Categorymentioning

confidence: 99%

Ensemble Learning Applications in Multiple Industries: A Review

Lin¹,

Huang²

2023

IDA

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COVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis

Dahal,

Rony,

Moulik

2024

Communications in Computer and Information Science

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Machine learning and deep learning covid-19 diagnosis system: key achievements, lessons learned, and a transfer learning algorithm

Salama,

Mohamed,

Abd-Ellah

2024

Soft Comput

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The COVID-19 pandemic is a novel, fast-spreading, deadly virus. It has spread around the world in an extremely short time. Due to its rapid spread and negative effects on all aspects of our lives (health, finances, stress, etc.), scientists are seeking to find accurate and fast solutions to this crisis. In our paper, we present a systematic literature review (SLR) of the different machine learning (ML) and deep learning (DL) techniques used for the detection, classification, and segmentation of COVID-19. We depend on our review of reliable databases such as IEEE Explore, Google Scholar, MDPI, Springer, PubMed, and Science Direct. By surveying approximately 978 papers, we found that 160 were more authorized, 77 of which were selected for review and met the criteria. A taxonomy is introduced to describe the sequence of our paper. Subsequently, a deep analysis and critical review of the academic literature were conducted to highlight the challenges and significant gaps identified in the introduced subject. The results revealed a shortage of research that assessed and established standards for the methods utilized for identifying and categorizing COVID-19 chest imaging techniques. As we continue the assessment and standardization process, three main difficulties are anticipated: the existence of various evaluation criteria for each task, the conflicts between these criteria, and the importance of these criteria. Moreover, we present a review of different systems used from the beginning of this crisis based on ML and DL by using different medical image modalities, such as chest X-ray, chest computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound imaging. We also highlight the datasets used and the different results of performance measures that have been developed by different researchers in this medical field. Finally, we discuss the limitations and lessons learned that are associated with the use of ML and DL techniques for diagnosing COVID-19. To support our work, we developed a new algorithm based on using transfer learning for several deep learning models and applied it to our own dataset. The aim of our paper is to collect various authorized data to help experts and specialists understand the importance of ML and DL systems in this respect, represent a new algorithm, and benefit them in future work toward fighting COVID-19.

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An intelligent deep convolutional network based COVID-19 detection from chest X-rays

Cited by 8 publications

References 61 publications

Ensemble Learning Applications in Multiple Industries: A Review

Ensemble Learning Applications in Multiple Industries: A Review

COVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis

Machine learning and deep learning covid-19 diagnosis system: key achievements, lessons learned, and a transfer learning algorithm

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