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  1. Article: COVID-19 infection map generation and detection from chest X-ray images.

    Degerli, Aysen / Ahishali, Mete / Yamac, Mehmet / Kiranyaz, Serkan / Chowdhury, Muhammad E H / Hameed, Khalid / Hamid, Tahir / Mazhar, Rashid / Gabbouj, Moncef

    Health information science and systems

    2021  Volume 9, Issue 1, Page(s) 15

    Abstract: ... to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray ... for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating ... COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed ...

    Abstract Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called
    Language English
    Publishing date 2021-04-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-021-00146-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: COVID-19 Infection Map Generation and Detection from Chest X-Ray Images

    Degerli, Aysen / Ahishali, Mete / Yamac, Mehmet / Kiranyaz, Serkan / Chowdhury, Muhammad E. H. / Hameed, Khalid / Hamid, Tahir / Mazhar, Rashid / Gabbouj, Moncef

    Abstract: ... methodologies, chest X-ray (CXR) imaging is an advantageous tool since it is fast, low-cost, and easily ... from CXR images by generating the so-called infection maps that can accurately localize and grade ... COVID-19) detection to aid treatment and prevent the spread of the virus. Compared to other diagnosis ...

    Abstract Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Compared to other diagnosis methodologies, chest X-ray (CXR) imaging is an advantageous tool since it is fast, low-cost, and easily accessible. Thus, CXR has a great potential not only to help diagnose COVID-19 but also to track the progression of the disease. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited CXR image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps that can accurately localize and grade the severity of COVID-19 infection. To accomplish this, we have compiled the largest COVID-19 dataset up to date with 2951 COVID-19 CXR images, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative expert human-machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 85.81%, that is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 98.37% sensitivity and 99.16% specificity.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  3. Book ; Online: COVID-19 Infection Map Generation and Detection from Chest X-Ray Images

    Degerli, Aysen / Ahishali, Mete / Yamac, Mehmet / Kiranyaz, Serkan / Chowdhury, Muhammad E. H. / Hameed, Khalid / Hamid, Tahir / Mazhar, Rashid / Gabbouj, Moncef

    2020  

    Abstract: ... to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray ... for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating ... COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed ...

    Abstract Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; covid19
    Subject code 006
    Publishing date 2020-09-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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