Identification of lung disease types using convolutional neural network and VGG-16 architecture
In: Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï, 2023-09-01, Heft 3, S. 96-107
Online
academicJournal
Zugriff:
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%.
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Identification of lung disease types using convolutional neural network and VGG-16 architecture
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Autor/in / Beteiligte Person: | Bukhori, Saiful ; Bangkit Yudho Negoro Verdy ; Yulia Retnani Windi Eka ; Adi Putra Januar |
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Zeitschrift: | Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï, 2023-09-01, Heft 3, S. 96-107 |
Veröffentlichung: | Igor Sikorsky Kyiv Polytechnic Institute, 2023 |
Medientyp: | academicJournal |
ISSN: | 1681-6048 (print) ; 2308-8893 (print) |
DOI: | 10.20535/SRIT.2308-8893.2023.3.07 |
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