COVID-19 Data Analytics Using Extended Convolutional Technique
The healthcare system, lifestyle, industrial growth, economy, and livelihood of human beings worldwide were affected due to the triggered global pandemic by the COVID-19 virus that originated and was first reported in Wuhan city, Republic Country of China. COVID cases are difficult to predict and de...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022-01-01
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| Series: | Interdisciplinary Perspectives on Infectious Diseases |
| Online Access: | http://dx.doi.org/10.1155/2022/4578838 |
| _version_ | 1811189311573327872 |
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| author | Anand Kumar Gupta Asadi Srinivasulu Olutayo Oyeyemi Oyerinde Giovanni Pau C. V. Ravikumar |
| author_facet | Anand Kumar Gupta Asadi Srinivasulu Olutayo Oyeyemi Oyerinde Giovanni Pau C. V. Ravikumar |
| author_sort | Anand Kumar Gupta |
| collection | DOAJ |
| description | The healthcare system, lifestyle, industrial growth, economy, and livelihood of human beings worldwide were affected due to the triggered global pandemic by the COVID-19 virus that originated and was first reported in Wuhan city, Republic Country of China. COVID cases are difficult to predict and detect in their early stages, and their spread and mortality are uncontrollable. The reverse transcription polymerase chain reaction (RT-PCR) is still the first and foremost diagnostical methodology accepted worldwide; hence, it creates a scope of new diagnostic tools and techniques of detection approach which can produce effective and faster results compared with its predecessor. Innovational through current studies that complement the existence of the novel coronavirus (COVID-19) to findings in the thorax (chest) X-ray imaging, the projected research’s method makes use of present deep learning (DL) models with the integration of various frameworks such as GoogleNet, U-Net, and ResNet50 to novel method those X-ray images and categorize patients as the corona positive (COVID + ve) or the corona negative (COVID -ve). The anticipated technique entails the pretreatment phase through dissection of the lung, getting rid of the environment which does now no longer provide applicable facts and can provide influenced consequences; then after this, the preliminary degree comes up with the category version educated below the switch mastering system; and in conclusion, consequences are evaluated and interpreted through warmth maps visualization. The proposed research method completed a detection accuracy of COVID-19 at around 99%. |
| first_indexed | 2024-04-11T14:33:55Z |
| format | Article |
| id | doaj.art-2bc1b19eeedb4b25b7a4a18d170f37b8 |
| institution | Directory Open Access Journal |
| issn | 1687-7098 |
| language | English |
| last_indexed | 2024-04-11T14:33:55Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Interdisciplinary Perspectives on Infectious Diseases |
| spelling | doaj.art-2bc1b19eeedb4b25b7a4a18d170f37b82022-12-22T04:18:26ZengHindawi LimitedInterdisciplinary Perspectives on Infectious Diseases1687-70982022-01-01202210.1155/2022/4578838COVID-19 Data Analytics Using Extended Convolutional TechniqueAnand Kumar Gupta0Asadi Srinivasulu1Olutayo Oyeyemi Oyerinde2Giovanni Pau3C. V. Ravikumar4Data Science Research LaboratoryData Science Research LaboratorySchool of Electrical and Information EngineeringKore University of EnnaSenseThe healthcare system, lifestyle, industrial growth, economy, and livelihood of human beings worldwide were affected due to the triggered global pandemic by the COVID-19 virus that originated and was first reported in Wuhan city, Republic Country of China. COVID cases are difficult to predict and detect in their early stages, and their spread and mortality are uncontrollable. The reverse transcription polymerase chain reaction (RT-PCR) is still the first and foremost diagnostical methodology accepted worldwide; hence, it creates a scope of new diagnostic tools and techniques of detection approach which can produce effective and faster results compared with its predecessor. Innovational through current studies that complement the existence of the novel coronavirus (COVID-19) to findings in the thorax (chest) X-ray imaging, the projected research’s method makes use of present deep learning (DL) models with the integration of various frameworks such as GoogleNet, U-Net, and ResNet50 to novel method those X-ray images and categorize patients as the corona positive (COVID + ve) or the corona negative (COVID -ve). The anticipated technique entails the pretreatment phase through dissection of the lung, getting rid of the environment which does now no longer provide applicable facts and can provide influenced consequences; then after this, the preliminary degree comes up with the category version educated below the switch mastering system; and in conclusion, consequences are evaluated and interpreted through warmth maps visualization. The proposed research method completed a detection accuracy of COVID-19 at around 99%.http://dx.doi.org/10.1155/2022/4578838 |
| spellingShingle | Anand Kumar Gupta Asadi Srinivasulu Olutayo Oyeyemi Oyerinde Giovanni Pau C. V. Ravikumar COVID-19 Data Analytics Using Extended Convolutional Technique Interdisciplinary Perspectives on Infectious Diseases |
| title | COVID-19 Data Analytics Using Extended Convolutional Technique |
| title_full | COVID-19 Data Analytics Using Extended Convolutional Technique |
| title_fullStr | COVID-19 Data Analytics Using Extended Convolutional Technique |
| title_full_unstemmed | COVID-19 Data Analytics Using Extended Convolutional Technique |
| title_short | COVID-19 Data Analytics Using Extended Convolutional Technique |
| title_sort | covid 19 data analytics using extended convolutional technique |
| url | http://dx.doi.org/10.1155/2022/4578838 |
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