Identification of lung disease types using convolutional neural network and VGG-16 architecture

Pneumonia, tuberculosis, and Covid-19 are different lung diseases but have similar characteristics. One of the reasons for the worsening of disease in lung sufferers is a diagnosis that takes a long time. Another factor, the results of the X-ray photos look blurry and lack contracture, causing diffe...

Full description

Saved in:

Bibliographic Details
Published in:	System research and information technologies no. 3; pp. 96 - 107
Main Authors:	Bukhori, Saiful, Verdy, Bangkit Yudho Negoro, Windi Eka, Yulia Retnani, Januar, Adi Putra
Format:	Journal Article
Language:	English
Published:	Igor Sikorsky Kyiv Polytechnic Institute 29-09-2023
Subjects:	convolutional neural network covid-19 pneumonia tuberculosis vgg-16
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Pneumonia, tuberculosis, and Covid-19 are different lung diseases but have similar characteristics. One of the reasons for the worsening of disease in lung sufferers is a diagnosis that takes a long time. Another factor, the results of the X-ray photos look blurry and lack contracture, causing different diagnostic results of X-ray photos. This research classifies lung images into four categories: normal lungs, tuberculosis, pneumonia, and Covid-19 using the Convolutional Neural Network method and VGG-16 architecture. The results of the research with models and scenarios without pre-trained use data with a ratio of 9:1 at epoch 50, an accuracy of 94%, while the lowest results are in scenarios using data with a ratio of 8:2 at epoch 50, non-pre-trained models, accuracy by 87%.
ISSN:	1681-6048 2308-8893
DOI:	10.20535/SRIT.2308-8893.2023.3.07