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Lung Inflammatory Classification of Diseases Using X-Ray Images

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Date

2021

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Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers Inc.

Open Access Color

Green Open Access

No

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Abstract

Recently, studies in inflammatory diseases categorization become of interest in the research community, especially with the sudden outbreak of the Covid-19 virus. Transfer learning proved to be the state-of-the-art when it comes to image classification problems, or related tasks. These methods achieve good results in this type of applications. Lately, this pre-trained embedding became even popular due to X-ray related studies for early Covid-19 diagnosis. In this study, we investigate the X-ray image classification problem using the transfer learning method. We fine-tuned and trained our model using pre-trained models such as AlexNet, VGG16, DenseNet etc, and a baseline deep neural network. We then evaluated this model in terms of classification evaluation metrics. The study shows that DenseNet achieves high accuracy compared to the other pre-trained and baseline CNN models. © 2021 IEEE

Description

Keywords

Chest X-Ray (Cxr), Data Augmentation, Deep Learning, Image Classification, Transfer Learning

Fields of Science

0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Citation

Mohanned, Hamza Haruna; Sürücü, Selim; Choupani, Roya. "Lung Inflammatory Classification of Diseases using X-ray Images", International Conference on Computer Science and Engineering (UBMK), 15-17 September 2021, Ankara.

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OpenCitations Citation Count
2

Source

Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 176826

Volume

Issue

Start Page

548

End Page

553
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CrossRef : 1

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Mendeley Readers : 2

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1

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5

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