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Moh. Rifkan
Universitas Dipa Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Implementasi Convolutional Neural Network terhadap Citra X-Ray Dada COVID-19 Berbasis Mobile Indo Intan; Suryani Suryani; ST Aminah Dinayati Ghani; Moh. Rifkan; Syamsul Bahri
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.640.625-641

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.
Co-Authors Muhammad Agus Muljanto ST. Aminah Dinayati Ghani Suryani Suryani Syamsul Bahri