Skip to main content Skip to main navigation menu Skip to site footer
Bulletin of Abai KazNPU. Series of Physical and mathematical sciences

BUILDING A MODEL FOR DETECTING PNEUMONIA USING DEEP LEARNING

Published September 2022

323

123

Б.С. Omarov+
Аl-Farabi Kazakh National University, Almaty, Kazakhstan
https://orcid.org/0000-0002-8341-7113
I.E. Bazarkulova+
Аl-Farabi Kazakh National University, Almaty, Kazakhstan
Аl-Farabi Kazakh National University, Almaty, Kazakhstan
Аl-Farabi Kazakh National University, Almaty, Kazakhstan
Abstract

The World Health Organization estimates that more than four million deaths occur annually due to pneumonia and other diseases associated with air pollution, and the latest Covid-19 virus has dramatically increased the percentage of pneumonia cases. There's also a global shortage of radiologists. Currently, the development of artificial intelligence and machine learning technologies, as well as the accumulation of large volumes of medical images, make it possible to create automated systems for analyzing medical images. The article presents a simple model based on deep learning methods (convolutional neural networks) that helps detect pneumonia. X-ray images of the women's and children's Medical Center in Guangzhou were used for the model. Training the neural network took 26 minutes. The results obtained in the test data are: recall – 96%, precision – 92%, accuracy – 92%, f1 – 94%. This is no less than the results in many popular works. The model significantly reduces the burden on radiologists, helps them make decisions and save time, allows to evaluate the quality of their work and reduce the likelihood of medical errors.

pdf (Русский)
Language

Қазақ

How to Cite

[1]
Омаров, Б. and Базаркулова, И. 2022. BUILDING A MODEL FOR DETECTING PNEUMONIA USING DEEP LEARNING. Bulletin of Abai KazNPU. Series of Physical and mathematical sciences. 79, 3 (Sep. 2022), 204–214. DOI:https://doi.org/10.51889/7671.2022.64.86.024.