Implementation of CNN of Mobile-based COVID-19 Chest X-Ray Images

Indo Intan; Suryani Suryani; ST Aminah Dinayati Ghani; Moh. Rifkan; Syamsul  Bahri

doi:10.31154/cogito.v10i1.640.625-641

Authors

Indo Intan Universitas Dipa Makassar http://orcid.org/0000-0003-2544-9691
Suryani Suryani Universitas Dipa Makassar
ST Aminah Dinayati Ghani Universitas Dipa Makassar
Moh. Rifkan Universitas Dipa Makassar
Syamsul Bahri Universitas Dipa Makassar

DOI:

https://doi.org/10.31154/cogito.v10i1.640.625-641

Keywords:

COVID-19, CNN, Image Analysis, Application, Android

Abstract

The COVID-19 pandemic outbreak is the most significant event from 2019 until 2021. A medical examination of radiological images is carried out to check the condition of the patient's lungs. The limitations of this examination need alternative computer-assisted applications for patient CXR. This research aims to implement a back-end and front-end-based Convolutional Neural Network (CNN) model. Its advantage is that it can detect CXR images in real-time and non-real-time using multi-classification, namely normal, pneumonia, and COVID-19. The CNN model carries out the process of convolutional feature extraction and multi-layer perceptron classification at the back-end stage. In contrast, it uses an Android mobile-based application at the front-end stage. The research results show that the non-real-time condition has an accuracy of 98%, while the real-time is 95% lower. This research produces model and application performance that is flexible for user needs. The results can be recommended for developing applications for more comprehensive users.

Author Biography

Indo Intan, Universitas Dipa Makassar

Department of Informatics Engineering

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