A flexible mobile application for image classification using deep learning: a case study on COVID-19 and X-ray images Online publication date: Fri, 11-Nov-2022
by Elilson Santos; Omar Andres Carmona Cortes; Bruno Feres De Souza
International Journal of Computer Applications in Technology (IJCAT), Vol. 69, No. 2, 2022
Abstract: This paper proposes a flexible mobile application for embedding any CNN-image-based classification model, providing a computer application to assist health professionals. Two approaches are suggested: an embedded offline and a running online model via web API. To present the applicability of the mobile software, we used a CNN COVID-19 classification based on X-ray images as a case study. Still, any other image-based classification application could have been used. We used a popular Kaggle database consisting of 7178 X-ray images divided into three classes: Normal, COVID-19, and Viral Pneumonia. We tested 14 state-of-art CNNs to decide which one to embed. The VGG16 achieved the best performance metrics; therefore, the VGG16 was embedded. The software production methodology was applied based on the built model, class diagram, use cases and execution flow, besides designing a web API to execute the back-end classification model.
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