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  1. Article ; Online: CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR.

    Monshi, Maram Mahmoud A / Poon, Josiah / Chung, Vera / Monshi, Fahad Mahmoud

    Computers in biology and medicine

    2021  Volume 133, Page(s) 104375

    Abstract: To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intelligence ( ...

    Abstract To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intelligence (AI) applications, in particular Convolutional Neural Network (CNN), can speed the COVID-19 diagnostic process. In this paper, we optimize the data augmentation and the CNN hyperparameters for detecting COVID-19 from CXRs in terms of validation accuracy. This optimization increases the accuracy of the popular CNN architectures such as the Visual Geometry Group network (VGG-19) and the Residual Neural Network (ResNet-50), by 11.93% and 4.97%, respectively. We then proposed CovidXrayNet model that is based on EfficientNet-B0 and our optimization results. We evaluated CovidXrayNet on two datasets, including our generated balanced COVIDcxr dataset (960 CXRs) and the benchmark COVIDx dataset (15,496 CXRs). With only 30 epochs of training, CovidXrayNet achieves state-of-the-art accuracy of 95.82% on the COVIDx dataset in the three-class classification task (COVID-19, normal or pneumonia). The CovidXRayNet model, the COVIDcxr dataset, and several optimization experiments are publicly available at https://github.com/MaramMonshi/CovidXrayNet.
    MeSH term(s) Artificial Intelligence ; COVID-19 ; Deep Learning ; Humans ; Neural Networks, Computer ; SARS-CoV-2 ; Tomography, X-Ray Computed
    Language English
    Publishing date 2021-04-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2021.104375
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  2. Article ; Online: Deep learning in generating radiology reports: A survey.

    Monshi, Maram Mahmoud A / Poon, Josiah / Chung, Vera

    Artificial intelligence in medicine

    2020  Volume 106, Page(s) 101878

    Abstract: Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than ... ...

    Abstract Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than traditional medical image annotation, or single sentence-based description, has been the subject of recent academic attention. This presents a more practical and challenging application and moves towards bridging visual medical features and radiologist text. So far, the most common approach has been to utilize publicly available datasets and develop DL models that integrate convolutional neural networks (CNN) for image analysis alongside recurrent neural networks (RNN) for natural language processing (NLP) and natural language generation (NLG). This is an area of research that we anticipate will grow in the near future. We focus our investigation on the following critical challenges: understanding radiology text/image structures and datasets, applying DL algorithms (mainly CNN and RNN), generating radiology text, and improving existing DL based models and evaluation metrics. Lastly, we include a critical discussion and future research recommendations. This survey will be useful for researchers interested in DL, particularly those interested in applying DL to radiology reporting.
    MeSH term(s) Algorithms ; Deep Learning ; Humans ; Natural Language Processing ; Neural Networks, Computer ; Radiology
    Keywords covid19
    Language English
    Publishing date 2020-05-15
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 645179-2
    ISSN 1873-2860 ; 0933-3657
    ISSN (online) 1873-2860
    ISSN 0933-3657
    DOI 10.1016/j.artmed.2020.101878
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2547: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
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    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Deep learning in generating radiology reports

    Monshi, Maram Mahmoud A. / Poon, Josiah / Chung, Vera

    A survey

    2020  

    Abstract: Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than ... ...

    Abstract Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than traditional medical image annotation, or single sentence-based description, has been the subject of recent academic attention. This presents a more practical and challenging application and moves towards bridging visual medical features and radiologist text. So far, the most common approach has been to utilize publicly available datasets and develop DL models that integrate convolutional neural networks (CNN) for image analysis alongside recurrent neural networks (RNN) for natural language processing (NLP) and natural language generation (NLG). This is an area of research that we anticipate will grow in the near future. We focus our investigation on the following critical challenges: understanding radiology text/image structures and datasets, applying DL algorithms (mainly CNN and RNN), generating radiology text, and improving existing DL based models and evaluation metrics. Lastly, we include a critical discussion and future research recommendations. This survey will be useful for researchers interested in DL, particularly those interested in applying DL to radiology reporting.
    Keywords COVID-19 ; Coronavirus ; covid19
    Subject code 410
    Language English
    Publishing date 2020-01-01
    Publishing country au
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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