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X-ray Based COVID-19 Classification Using Lightweight EfficientNet

Journal on Artificial Intelligence 2022年第3期4卷 167-187页

作者： tahani maazi almutairi Mohamed Maher Ben Ismail Ouiem Bchir Department of Computer Science King Saud UniversityRiyadh11543Saudi Arabia

The world has been suffering from the Coronavirus(COVID-19)pandemic since its appearance in late ***-19 spread has led to a drastic increase of the number of infected people and deaths *** and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the *** this paper,we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 ***,we adapted lightweight versions of EfficientNet as backbone of the proposed recognition ***,lightweight EfficientNet networks were used to build classification models able to discriminate between positive and negative COVID-19 cases using chest X-ray *** proposed models ensure a trade-off between scaling down the architecture of the deep network to reduce the computational cost and optimizing the classification *** the experiments,a public dataset containing 7,345 chest X-ray images was used to train,validate and test the proposed models for binary and multiclass classification problems,*** obtained results showed the EfficientNet-elite-B9-V2,which is the lightest proposed model yielded an accuracy of 96%.On the other hand,EfficientNet-lite-B0 overtook the other models,and achieved an accuracy of 99%.

关键词： CNN EfficientNet COVID-19 deep learning CAD system X-ray

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LRV: A Tool for Academic Text Visualization to Support theLiterature Review Process

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Computers, Materials & Continua 2019年第6期59卷 741-751页

作者： tahani almutairi Maha Al-yahya Information Technology Department College of Computer and Information SciencesKing Saud UniversityDeriyah CampusRiyadh 11451Saudi Arabia Information Technology Department College of Computer and Information SciencesKing Saud.UniversityDeriyah CampusRiyadh 11451Saudi Arabia

Text visualization is concerned with the representation of text in a graphicalform to facilitate comprehension of large textual data. Its aim is to improve the ability tounderstand and utilize the wealth of text-based information available. An essential task inany scientific research is the study and review of previous works in the specified domain,a process that is referred to as the literature survey process. This process involves theidentification of prior work and evaluating its relevance to the research question. With theenormous number of published studies available online in digital form, this becomes acumbersome task for the researcher. This paper presents the design and implementationof a tool that aims to facilitate this process by identifying relevant work and suggestingclusters of articles by conceptual modeling, thus providing different options that enablethe researcher to visualize a large number of articles in a graphical easy-to-analyze *** tool helps the researcher in analyzing and synthesizing the literature and building aconceptual understanding of the designated research area. The evaluation of the toolshows that researchers have found it useful and that it supported the process of relevantwork analysis given a specific research question, and 70% of the evaluators of the toolfound it very useful.

关键词： Text visualization information extraction text mining literature review

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