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Published in: New Generation Computing 1/2023

20-11-2022

Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images

Authors: Ankit Kumar Dubey, Krishna Kumar Mohbey

Published in: New Generation Computing | Issue 1/2023

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Abstract

In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.

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Metadata
Title
Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images
Authors
Ankit Kumar Dubey
Krishna Kumar Mohbey
Publication date
20-11-2022
Publisher
Springer Japan
Published in
New Generation Computing / Issue 1/2023
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-022-00195-x

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