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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Mahmoud Masadeh
Yarmouk University
Computer Science & IT Engineering

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An efficient machine learning-based COVID-19 identification utilizing chest X-ray images Mahmoud Masadeh; Ayah Masadeh; Omar Alshorman; Falak H Khasawneh; Mahmoud Ali Masadeh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp356-366

Abstract

There is no well-known vaccine for coronavirus disease (COVID-19) with 100% efficiency. COVID-19 patients suffer from a lung infection, where lung-related problems can be effectively diagnosed with image techniques. The golden test for COVID-19 diagnosis is the RT-PCR test, which is costly, time-consuming and unavailable for various countries. Thus, machine learning-based tools are a viable solution. Here, we used a labelled chest X-ray of three categories, then performed data cleaning and augmentation to use the data in deep learning-based convolutional neural network (CNN) models. We compared the performance of different models that we gradually built and analyzed their accuracy. For that, we used 2905 chest X-ray scan samples. We were able to develop a model with the best accuracy of 97.44% for identifying COVID-19 using X-ray images. Thus, in this paper, we attested the feasibility of efficiently applying machine learning (ML) based models for medical image classification.
A review of remote health monitoring based on internet of things Omar AlShorman; Buthaynah Alshorman; Mahmoud Masadeh; Fahad Alkahtani; Basim Al-Absi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp297-306

Abstract

Managing, diagnosis, prognosis, continuous monitoring, early detection, and preventing chronic diseases for patients and elderly people have been gained a crucial role nowadays. However, elderly people with chronic health conditions such as diabetes, cardiovascular disease, and mental diseases, need special health care. With the help of the internet of things (IoT) technologies, remote health monitoring (RHM) helps patients, caregivers, and countries for improving healthcare services, such as medical files services, mobile healthcare (mhealth), telemedicine services, and sensing technology. Moreover, RHM aims to reduce hospitalized demands and costs. The main contribution of the proposed study is to review RHM studies based on IoT technologies. Moreover, the challenges and possible future trends of RMH are highlighted.
Co-Authors Ayah Masadeh Basim Al-Absi Buthaynah Alshorman Fahad Alkahtani Falak H Khasawneh Mahmoud Ali Masadeh Omar Alshorman