Classification of Radiographic Images of chest of COVID-19 Patients, Pneumonia affected and Normal Patients through ANN
Siddharth Krushnarao Ganvir
, Dr.V.L.Agrawal
MatLab, Neuro Solution Software, Microsoft excel, Various Transform Technique
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
"Classification of Radiographic Images of chest of COVID-19 Patients, Pneumonia affected and Normal Patients through ANN", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.6, Issue 6, page no.339 - 345, June-2021, Available :https://ijsdr.org/papers/IJSDR2106048.pdf
Volume 6
Issue 6,
June-2021
Pages : 339 - 345
Paper Reg. ID: IJSDR_193433
Published Paper Id: IJSDR2106048
Downloads: 000347181
Research Area: Engineering
Country: Amravati, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
