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Beyond Binary Diagnostics of Pneumonia Detection with Deep Learning

by Olukemi Victoria Olatunde, Olumide Sunday Adewale, Parimala Thulasiraman, Oladunni Abosede Daramola
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 44
Year of Publication: 2024
Authors: Olukemi Victoria Olatunde, Olumide Sunday Adewale, Parimala Thulasiraman, Oladunni Abosede Daramola
10.5120/ijais2024451972

Olukemi Victoria Olatunde, Olumide Sunday Adewale, Parimala Thulasiraman, Oladunni Abosede Daramola . Beyond Binary Diagnostics of Pneumonia Detection with Deep Learning. International Journal of Applied Information Systems. 12, 44 ( May 2024), 22-28. DOI=10.5120/ijais2024451972

@article{ 10.5120/ijais2024451972,
author = { Olukemi Victoria Olatunde, Olumide Sunday Adewale, Parimala Thulasiraman, Oladunni Abosede Daramola },
title = { Beyond Binary Diagnostics of Pneumonia Detection with Deep Learning },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2024 },
volume = { 12 },
number = { 44 },
month = { May },
year = { 2024 },
issn = { 2249-0868 },
pages = { 22-28 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number44/beyond-binary-diagnostics-of-pneumonia-detection-with-deep-learning/ },
doi = { 10.5120/ijais2024451972 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-05-30T21:54:12.321269+05:30
%A Olukemi Victoria Olatunde
%A Olumide Sunday Adewale
%A Parimala Thulasiraman
%A Oladunni Abosede Daramola
%T Beyond Binary Diagnostics of Pneumonia Detection with Deep Learning
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 44
%P 22-28
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Pneumonia has been a major public health issue throughout human history and is among the common symptoms of the virus that causes COVID-19, which has turned into a worldwide pandemic. The disease is especially prevalent in developing countries, with Nigeria being one of the five countries accounting for more than half of the world’s annual incident cases of pneumonia. In 2015, Nigeria recorded 2,300 deaths among children under five due to pneumonia, and it is projected that two million could die in the next decade if no action is taken. The disease wreaks havoc in areas where the doctor-to-patient ratio is low, causing deaths primarily in children under five years old and elderly people over 65 years old, particularly those with weakened immune systems. Pneumonia is the leading infectious cause of death in children worldwide, with higher incidence rates in developing countries due to poor sanitary conditions and poverty. Deep learning techniques, particularly Convolutional Neural Networks (CNNs) using X-ray images, have significantly contributed to pneumonia diagnosis. However, previous studies have only focused on distinguishing between healthy and pneumonia patients without considering the severity level. In developing countries, physicians using these machine learning tools are unable to determine the severity of pneumonia, resulting in patients with moderate pneumonia not receiving adequate care. Although this approach is cost-effective for the healthcare system, it is dangerous for the patients. In this study, a deep learning model is proposed to complement the work of physicians by determining the severity level of pneumonia after confirming the infection through X-ray.

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Index Terms

Computer Science
Information Sciences

Keywords

Pneumonia Severity HOG CNN X-ray images Pearson Correlation

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