Yıl 2021, Cilt 4 , Sayı 1, Sayfalar 26 - 34 2021-04-30

Detection of Pneumonia with a Novel CNN-based Approach

Ebru ERDEM [1] , Tolga AYDİN [2]


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%)).
Pneumonia, VGG16, VGG19, CNN
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Birincil Dil en
Konular Bilgisayar Bilimleri, Yapay Zeka
Bölüm Makaleler
Yazarlar

Orcid: 0000-0002-4042-7549
Yazar: Ebru ERDEM (Sorumlu Yazar)
Kurum: ATATURK UNIVERSITY
Ülke: Turkey


Orcid: 0000-0002-8971-3255
Yazar: Tolga AYDİN
Kurum: ATATURK UNIVERSITY
Ülke: Turkey


Tarihler

Başvuru Tarihi : 28 Ağustos 2020
Kabul Tarihi : 28 Aralık 2020
Yayımlanma Tarihi : 30 Nisan 2021

Bibtex @araştırma makalesi { saucis787030, journal = {Sakarya University Journal of Computer and Information Sciences}, issn = {}, eissn = {2636-8129}, address = {}, publisher = {Sakarya Üniversitesi}, year = {2021}, volume = {4}, pages = {26 - 34}, doi = {10.35377/saucis.04.01.787030}, title = {Detection of Pneumonia with a Novel CNN-based Approach}, key = {cite}, author = {Erdem, Ebru and Aydin, Tolga} }
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 . DOI: 10.35377/saucis.04.01.787030
MLA Erdem, E , Aydin, T . "Detection of Pneumonia with a Novel CNN-based Approach" . Sakarya University Journal of Computer and Information Sciences 4 (2021 ): 26-34 <http://saucis.sakarya.edu.tr/tr/pub/issue/59732/787030>
Chicago Erdem, E , Aydin, T . "Detection of Pneumonia with a Novel CNN-based Approach". Sakarya University Journal of Computer and Information Sciences 4 (2021 ): 26-34
RIS TY - JOUR T1 - Detection of Pneumonia with a Novel CNN-based Approach AU - Ebru Erdem , Tolga Aydin Y1 - 2021 PY - 2021 N1 - doi: 10.35377/saucis.04.01.787030 DO - 10.35377/saucis.04.01.787030 T2 - Sakarya University Journal of Computer and Information Sciences JF - Journal JO - JOR SP - 26 EP - 34 VL - 4 IS - 1 SN - -2636-8129 M3 - doi: 10.35377/saucis.04.01.787030 UR - https://doi.org/10.35377/saucis.04.01.787030 Y2 - 2020 ER -
EndNote %0 Sakarya University Journal of Computer and Information Sciences Detection of Pneumonia with a Novel CNN-based Approach %A Ebru Erdem , Tolga Aydin %T Detection of Pneumonia with a Novel CNN-based Approach %D 2021 %J Sakarya University Journal of Computer and Information Sciences %P -2636-8129 %V 4 %N 1 %R doi: 10.35377/saucis.04.01.787030 %U 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 (Nisan 2021): 26-34 . https://doi.org/10.35377/saucis.04.01.787030
AMA Erdem E , Aydin T . Detection of Pneumonia with a Novel CNN-based Approach. SAUCIS. 2021; 4(1): 26-34.
Vancouver Erdem E , Aydin T . Detection of Pneumonia with a Novel CNN-based Approach. Sakarya University Journal of Computer and Information Sciences. 2021; 4(1): 26-34.
IEEE E. Erdem ve T. Aydin , "Detection of Pneumonia with a Novel CNN-based Approach", Sakarya University Journal of Computer and Information Sciences, c. 4, sayı. 1, ss. 26-34, Nis. 2021, doi:10.35377/saucis.04.01.787030