Detection of Pneumonia with a Novel CNN-based Approach

Ebru Erdem; Tolga Aydin

doi:10.35377/saucis.04.01.787030

EN

Detection of Pneumonia with a Novel CNN-based Approach

Abstract

Pneumonia is a seasonal infectious lung tissue inflammatory disease. According to the World Health Organization (WHO), early diagnosis of the disease reduces the risk of its transmission and death. Various deep learning and machine learning algorithms were used for pneumonia detection. This study aims to analyze the lung images and diagnose pneumonia disease by employing deep learning approaches. We have suggested a novel deep learning framework for the detection of pneumonia in lung. A comparison was made between the proposed new deep learning model and pre-trained deep learning models. 88.62% accuracy rate has been obtained from the proposed deep learning structure. It was observed that by utilizing the new deep neural network developed, the accuracy results of VGG16 (88.78%) and VGG19 (88.30%), which are among the popular deep learning architectures, can be approximated. The test results show that our proposed model has a better recall value (97.43%) (VGG16 (93.33%) and VGG19 (96.92%)), and a better F1-Score (91.45%) (VGG16 (91.22%) and VGG19 (91.19%)).

Keywords

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Ebru Erdem ^*
0000-0002-4042-7549
Türkiye

Tolga Aydin
0000-0002-8971-3255
Türkiye

Publication Date

April 30, 2021

Submission Date

August 28, 2020

Acceptance Date

December 28, 2020

Published in Issue

Year 1970 Volume: 4 Number: 1

DOI

https://doi.org/10.35377/saucis.04.01.787030

IZ

https://izlik.org/JA98HB86HE

Cite

RIS / Bibtex

APA

Erdem, E., & Aydin, T. (2021). Detection of Pneumonia with a Novel CNN-based Approach. Sakarya University Journal of Computer and Information Sciences, 4(1), 26-34. https://doi.org/10.35377/saucis.04.01.787030

AMA

1.Erdem E, Aydin T. Detection of Pneumonia with a Novel CNN-based Approach. SAUCIS. 2021;4(1):26-34. doi:10.35377/saucis.04.01.787030

Chicago

Erdem, Ebru, and Tolga Aydin. 2021. “Detection of Pneumonia With a Novel CNN-Based Approach”. Sakarya University Journal of Computer and Information Sciences 4 (1): 26-34. https://doi.org/10.35377/saucis.04.01.787030.

EndNote

Erdem E, Aydin T (April 1, 2021) Detection of Pneumonia with a Novel CNN-based Approach. Sakarya University Journal of Computer and Information Sciences 4 1 26–34.

IEEE

[1]E. Erdem and T. Aydin, “Detection of Pneumonia with a Novel CNN-based Approach”, SAUCIS, vol. 4, no. 1, pp. 26–34, Apr. 2021, doi: 10.35377/saucis.04.01.787030.

ISNAD

Erdem, Ebru - Aydin, Tolga. “Detection of Pneumonia With a Novel CNN-Based Approach”. Sakarya University Journal of Computer and Information Sciences 4/1 (April 1, 2021): 26-34. https://doi.org/10.35377/saucis.04.01.787030.

JAMA

1.Erdem E, Aydin T. Detection of Pneumonia with a Novel CNN-based Approach. SAUCIS. 2021;4:26–34.

MLA

Erdem, Ebru, and Tolga Aydin. “Detection of Pneumonia With a Novel CNN-Based Approach”. Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, Apr. 2021, pp. 26-34, doi:10.35377/saucis.04.01.787030.

Vancouver

1.Ebru Erdem, Tolga Aydin. Detection of Pneumonia with a Novel CNN-based Approach. SAUCIS. 2021 Apr. 1;4(1):26-34. doi:10.35377/saucis.04.01.787030

Cited By

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Detection of Pneumonia with a Novel CNN-based Approach

Öz

Detection of Pneumonia with a Novel CNN-based Approach

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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