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  1. Article ; Online: Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective.

    Vinod, Dasari Naga / Prabaharan, S R S

    Scientific African

    2023  Volume 20, Page(s) e01681

    Abstract: Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical ... ...

    Abstract Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of a threshold-based image segmentation technique and random forest classifier for COVID-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify COVID-19 individuals into three asperity classes such as early, progressive, and advanced, with an accuracy of 95.5% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify coronavirus severity.
    Language English
    Publishing date 2023-05-01
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2468-2276
    ISSN (online) 2468-2276
    DOI 10.1016/j.sciaf.2023.e01681
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Elucidation of infection asperity of CT scan images of COVID-19 positive cases

    Dasari Naga Vinod / S.R.S. Prabaharan

    Scientific African, Vol 20, Iss , Pp e01681- (2023)

    A Machine Learning perspective

    2023  

    Abstract: Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical ... ...

    Abstract Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of a threshold-based image segmentation technique and random forest classifier for COVID-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify COVID-19 individuals into three asperity classes such as early, progressive, and advanced, with an accuracy of 95.5% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify coronavirus severity.
    Keywords Coronavirus ; Computed tomography ; Severity ; Lung disjunction ; Machine Learning ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2023-07-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.

    Vinod, Dasari Naga / Prabaharan, S R S

    Archives of computational methods in engineering : state of the art reviews

    2023  Volume 30, Issue 4, Page(s) 2667–2682

    Abstract: The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. ...

    Abstract The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.
    Language English
    Publishing date 2023-01-17
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 2276736-8
    ISSN 1886-1784 ; 1134-3060
    ISSN (online) 1886-1784
    ISSN 1134-3060
    DOI 10.1007/s11831-023-09882-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19.

    Vinod, Dasari Naga / Prabaharan, S R S

    Chaos, solitons, and fractals

    2020  Volume 140, Page(s) 110182

    Abstract: The rapid spread of novel coronavirus (namely Covid-19) worldwide has alarmed a pandemic since its outbreak in the city of Wuhan, China in December 2019. While the world still tries to wrap its head around as to how to contain the rapid spread of the ... ...

    Abstract The rapid spread of novel coronavirus (namely Covid-19) worldwide has alarmed a pandemic since its outbreak in the city of Wuhan, China in December 2019. While the world still tries to wrap its head around as to how to contain the rapid spread of the novel coronavirus, the pandemic has already claimed several thousand lives throughout the world. Yet, the diagnosis of virus spread in humans has proven complexity. A blend of computed tomography imaging, entire genome sequencing, and electron microscopy have been at first adapted to screen and distinguish SARS-CoV-2, the viral etiology of Covid-19. There are a less number of Covid-19 test kits accessible in hospitals because of the expanding cases every day. Accordingly, it is required to utensil a self-exposure framework as a fast substitute analysis to contain Covid-19 spreading among individuals considering the world at large. In the present work, we have elaborated a prudent methodology that helps identify Covid-19 infected people among the normal individuals by utilizing CT scan and chest x-ray images using Artificial Intelligence (AI). The strategy works with a dataset of Covid-19 and normal chest x-ray images. The image diagnosis tool utilizes decision tree classifier for finding novel corona virus infected person. The percentage accuracy of an image is analyzed in terms of precision, recall score and F1 score. The outcome depends on the information accessible in the store of Kaggle and Open-I according to their approved chest X-ray and CT scan images. Interestingly, the test methodology demonstrates that the intended algorithm is robust, accurate and precise. Our technique accomplishes the exactness focused on the AI innovation which provides faster results during both training and inference.
    Keywords covid19
    Language English
    Publishing date 2020-07-30
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110182
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19

    Vinod, Dasari Naga / Prabaharan, S.R.S.

    Chaos, Solitons & Fractals

    2020  Volume 140, Page(s) 110182

    Keywords General Mathematics ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110182
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model.

    Vinod, Dasari Naga / Jeyavadhanam, B Rebecca / Zungeru, Adamu Murtala / Prabaharan, S R S

    Computers in biology and medicine

    2021  Volume 136, Page(s) 104729

    Abstract: SARS-COV2 (Covid-19) prevails in the form of multiple mutant variants causing pandemic situations around the world. Thus, medical diagnosis is not accurate. Although several clinical diagnostic methodologies have been introduced hitherto, chest X-ray and ...

    Abstract SARS-COV2 (Covid-19) prevails in the form of multiple mutant variants causing pandemic situations around the world. Thus, medical diagnosis is not accurate. Although several clinical diagnostic methodologies have been introduced hitherto, chest X-ray and computed tomography (CT) imaging techniques complement the analytical methods (for instance, RT-PCR) to a certain extent. In this context, we demonstrate a novel framework by employing various image segmentation models to leverage the available image databases (9000 chest X-ray images and 6000 CT scan images). The proposed methodology is expected to assist in the prognosis of Covid-19-infected individuals through examination of chest X-rays and CT scans of images using the Deep Covix-Net model for identifying novel coronavirus-infected patients effectively and efficiently. The slice of the precision score is analysed in terms of performance metrics such as accuracy, the confusion matrix, and the receiver operating characteristic curve. The result leans on the database obtainable in the GitHub and Kaggle repository, conforming to their endorsed chest X-ray and CT images. The classification performances of various algorithms were examined for a test set with 1800 images. The proposed model achieved a 96.8% multiple-classification accuracy among Covid-19, normal, and pneumonia chest X-ray databases. Moreover, it attained a 97% accuracy among Covid-19 and normal CT scan images. Thus, the proposed mechanism achieves the rigorousness associated with the machine learning technique, providing rapid outcomes for both training and testing datasets.
    MeSH term(s) COVID-19 ; Deep Learning ; Humans ; RNA, Viral ; SARS-CoV-2 ; Tomography, X-Ray Computed ; X-Rays
    Chemical Substances RNA, Viral
    Language English
    Publishing date 2021-08-03
    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.2021.104729
    Database MEDical Literature Analysis and Retrieval System OnLINE

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