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Comparison and Evaluation of CNN Architectures for Classification of Covid-19 and Pneumonia
Mahadar A., Mangukiya P.,
Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 2021-November
Pages: 110 - 115
At present, India has the largest population of below 14 years children in the Asia Pacific. With the increasing birth rate, critical Pneumonia cases have been referred to Neonatal Hospital for treatment. As the number of adults in India who have tested positive for COVID-19 has grown, so has the number of children who have contracted it. However, we haven't noticed a dramatic increase in the number of children infected with COVID-19 across the country. It's important to note that, unlike the previous wave, the second wave is more likely to infect whole homes. We must be vigilant and adhere to COVID-19's recommended practices. The current study says that the mortality rate of Pneumonia and Covid-19 infection in rural areas is high. Radiology plays a vital role to diagnose Pneumonia by the examination of X-Ray images. Over the years CNN Architectures have evolved and now produce appreciable accuracy (over 85%) for classification tasks. This has promoted the use of CNN Architectures in the field of medicine, especially for classification tasks such as disease detection from x-rays. This implementation evaluates the performance of four popular CNN Architectures viz. VGG16, ResNet50V2, InceptionV3 and MobileNetV2. The implementation will classify x-ray images into normal, covid, and pneumonia and then compare the performance of the aforementioned models over the accuracy, Area under the curve (AUC), precision, recall metrics. © 2021 IEEE.
About the journal
JournalProceedings of the IEEE International Conference Image Information Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo