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Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters

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Applied Informatics (ICAI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1643))

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Abstract

As of 2019, COVID-19 is the most difficult issue that we are facing. Till now, it has reached over 30 million deaths. Since SARS-CoV-2 is the new virus, it took time to investigate and examine the influence of Coronavirus in human. After analyzing the spreading and infection of COVID-19, researchers applied Artificial Intelligence (AI) techniques to detect COVID-19 quickly to balance the rapid spreading of the virus. Image segmentation is a critical first step in clinical implementations, is a vital role in computer - aided diagnosis that relies heavily on image recognition. Image segmentation is used in medical MRI research to determine the proportions of different anatomical areas of the tissue, as well as how they change as the disease progresses. CT scans are often used to aid with diagnoses. Computer-assisted therapy (CAD) using AI is a particularly significant research area in intelligent healthcare. This paper presents the detection of COVID-19 at an early stage using autoencoders algorithm and Generative Adversarial Networks (GAN) using deep learning approach with more accurate results. The images of Chest Radiograph (CRG) and Chest Computed Tomography (CCT) are used as a trained dataset to detect since SARS-CoV-2 first affect the respiratory system in humans. We achieved a ratio of 1.0, 0.99, and 0.96, the combined dataset was randomly divided into the train, validation, and test sets. Although the early detection of Coronavirus is still a question since the accuracy of the deep learning approach is still under research.

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Authors and Affiliations

  1. IFET College of Engineering, Tamil Nadu, Villupuram, 605108, India

    Jayalakshmi Saravanan & T. Ananth Kumar

  2. Department of Data Science, University of Salford, Greater Manchester, M5 4WT, UK

    Andrew C. Nwanakwaugwu & Oluwafemi O. Olawoyin

  3. Department of Computer Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

    Sunday Adeola Ajagbe, Ademola T. Opadotun & Deborah D. Afolayan

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  1. Jayalakshmi Saravanan
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  2. T. Ananth Kumar
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  3. Andrew C. Nwanakwaugwu
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  4. Sunday Adeola Ajagbe
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  5. Ademola T. Opadotun
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  6. Deborah D. Afolayan
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  7. Oluwafemi O. Olawoyin
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Correspondence to Sunday Adeola Ajagbe .

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Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Universidad Continental, Arequipa, Peru

    Henry Gomez

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Saravanan, J. et al. (2022). Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters. In: Florez, H., Gomez, H. (eds) Applied Informatics. ICAI 2022. Communications in Computer and Information Science, vol 1643. Springer, Cham. https://doi.org/10.1007/978-3-031-19647-8_18

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  • DOI: https://doi.org/10.1007/978-3-031-19647-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19646-1

  • Online ISBN: 978-3-031-19647-8

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