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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Classification of Radiographic Images of chest of COVID-19 Patients, Pneumonia affected and Normal Patients through ANN |
| Authors Name: | Siddharth Krushnarao Ganvir , Dr.V.L.Agrawal |
| Unique Id: | IJSDR2106048 |
| Published In: | Volume 6 Issue 6, June-2021 |
| Abstract: | With the exponentially growing COVID-19 (corona virus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, we propose a viable and efficient deep learning-based chest radiograph framework to analyze COVID-19 cases with accuracy. A unique dataset is prepared from available sources containing the chest view of CT scan/X-ray data for COVID-19 cases. Our proposed framework leverages a data augmentation of radiograph images algorithm for the COVID-19 data, by adaptively employing the MATLAB and NeuroSolution on COVID-19 infected chest images to generate a train a robust model. The training data consisting of actual and synthetic chest images are fed into our customized neural network model, which achieves COVID-19 detection with good accuracy. Furthermore, through this it is possible to efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities. Index Terms - MatLab, Neuro Solution Software, Microsoft excel, Various Transform Technique |
| Keywords: | MatLab, Neuro Solution Software, Microsoft excel, Various Transform Technique |
| Cite Article: | "Classification of Radiographic Images of chest of COVID-19 Patients, Pneumonia affected and Normal Patients through ANN", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.6, Issue 6, page no.339 - 345, June-2021, Available :http://www.ijsdr.org/papers/IJSDR2106048.pdf |
| Downloads: | 000223215 |
| Publication Details: | Published Paper ID: IJSDR2106048 Registration ID:193433 Published In: Volume 6 Issue 6, June-2021 DOI (Digital Object Identifier): Page No: 339 - 345 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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