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Data Science in Healthcare Monitoring Under Covid-19 Detection by Extended Hybrid Leader-Based Compressed Neural Network

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Abstract

The Severe Acute Respiratory Syndrome CoronoVirus2 (SARS-CoV-2) causes the infectious illness Covid-19 (Corona Virus Disease of 2019). The majority of virus-infected persons have mild to moderate respiratory illness and recover on their own. However, some people get serious ailments and need medical attention. Covid-19 can potentially make anyone seriously ill or cause passing away at any age. Nowadays, medical science is based on data science technology to achieve new milestones in genetics, genomics, patient-customer assistance, clinical imaging, drug discovery, and predictive medicine. It has gained due to Covid-19. This paper proposes a novel method: Extended and Compressed Convolution Hybrid Leader-based Optimization (ECCHLO) to differentiate between Covid-19 infected persons and healthy individuals. This outline involves four steps. The initial step is preprocessing the X-ray and Computed Tomography (CT) scan images gathered from the datasets. In the second step, feature extraction is performed. Next, feature fusion is performed to eliminate redundant and unnecessary data. Finally, the Extended Hybrid Leader-Based Optimization (EHLBO) algorithm is utilized to tune the parameters of compressed convolutional neural networks. The ECCHLO model is examined and tested on four datasets, namely SARS-COV-2, Covid-19 radiography database, Covid-CT, and Covid-19 Image datasets. The accuracy of these four datasets is 99.6%, 99.5%, 99.3%, and 100%, respectively, which are higher than the other techniques. This demonstrates that the ECCHLO model is reliable and stable for identifying a person infected with Covid-19.

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Data sharing is not applicable to this article as no datasets were generated.

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  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India

    Asha Latha Thandu & Pradeepini Gera

  2. Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, 501301, India

    Vijaya Saradhi Thommandru

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  1. Asha Latha Thandu
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Thandu, A.L., Thommandru, V.S. & Gera, P. Data Science in Healthcare Monitoring Under Covid-19 Detection by Extended Hybrid Leader-Based Compressed Neural Network. New Gener. Comput. 41, 669–696 (2023). https://doi.org/10.1007/s00354-023-00225-2

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