A flexible mobile application for image classification using deep learning: a case study on COVID-19 and X-ray images. (7th November 2022)

• Record Type: Journal Article
• Authors: Santos, Elilson; Cortes, Omar Andres Carmona; Souza, Bruno Feres De
• Is Part Of: International journal of computer applications technology. Volume 69:Number 2(2022)
• Page Start: 150
• Page End: 162
• Publication Date: 2022-11-07
• Subjects: mobile application -- medicine 40 -- CNN -- COVID-19 -- X-ray; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

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.