Medical Images Application for X-Ray Image Classification Based on Convolution Neural Network

DOI

10.2991/978-94-6463-620-8_18How to use a DOI?

Keywords

Lungs; X-Ray Imaging; Convolutional Neural Networks; DenseNet121

Abstract

The lungs are vital organs for the respiration of the human body. If it’s not correctly observed, it can lead to disease in this organ. It’s like pneumonia, where one child dies every 39 seconds. Indoseia ranks third after India and China in the case of tuberculosis disease. During the COVID-19 pandemic, 6.812.127 confirmed cases were reported in Indonesia. The abnormalities in the lung can be seen through the X-ray imaging. X-ray is a medical procedure that uses waves of X-ray radiation with a grayscale color type. When a doctor analyses a large number of X-ray images, it’s exhausting and requires a high level of focus. Convolutional Neural Networks (CNN) are widely used today to classify images or videos. Many researchers have used CNN architecture methods to read and identify diseases from X-ray images, as well as using CNN’s derivative architecture, DenseNet121, to re-train the CNN output model, which aims to improve the effectiveness of models in classifying X-ray images. This research aims to create a Graphical User Interface (GUI) that implements the CNN classification results. It can read X-rays not only one but more than two at a time, and the identification process is fast and easy to operate. Also, the accuracy of the results of the reading of X-ray images, as the final result of this research process, obtained a result of 92% accuracy. The model is integrated with the Graphical User Interface (GUI), which can perform classification against four conditions on X-ray images.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Budi Sugandi
AU  - Galang Samudra
PY  - 2024
DA  - 2024/12/25
TI  - Medical Images Application for X-Ray Image Classification Based on Convolution Neural Network
BT  - Proceedings of the  7th International Conference on Applied Engineering (ICAE 2024)
PB  - Atlantis Press
SP  - 233
EP  - 247
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-620-8_18
DO  - 10.2991/978-94-6463-620-8_18
ID  - Sugandi2024
ER  -