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Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019003" target="_blank" >RIV/62690094:18470/22:50019003 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.techscience.com/cmc/v71n1/45388" target="_blank" >https://www.techscience.com/cmc/v71n1/45388</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.32604/cmc.2022.020820" target="_blank" >10.32604/cmc.2022.020820</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

  • Original language description

    This research article proposes an automatic frame work for detecting COVID-19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    CMC-Computers, Materials &amp; Continua

  • ISSN

    1546-2218

  • e-ISSN

    1546-2226

  • Volume of the periodical

    71

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    16

  • Pages from-to

    650-665

  • UT code for WoS article

    000717617700037

  • EID of the result in the Scopus database

    2-s2.0-85118540112