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Göğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılması

Year 2024, Volume: 39 Issue: 2, 729 - 740, 30.11.2023

Zehra Kadiroğlu Erkan Deniz Abdurrahman Şenyiğit

https://doi.org/10.17341/gazimmfd.1204092

Abstract

Pnömoni, akciğer dokusunda ciddi iltihaplanmalara sebep olabilen akut alt solunum yolu hastalıklarından biridir. Pnömoni tanısı için en yaygın klinik yöntem göğüs röntgeni (CXR) olmakla beraber, CXR görüntülerinden pnömoni teşhisi, uzman radyologlar için bile zor bir iştir. Derin öğrenme tabanlı görüntü işlemenin, pnömoni’nin otomatik teşhisinde etkili olduğu literatürdeki çalışmalarda gösterilmiştir. Bu çalışmada pnömoni ve sağlıklı CXR görüntülerini sınıflandırmak için derin öğrenmeye dayalı yaklaşımlar kullanılmıştır. Bu yaklaşımlar, derin öznitelik çıkarımı, önceden eğitilmiş evrişimli sinir ağlarının (ESA) ince ayarı ve geliştirilmiş bir ESA modelinin uçtan uca eğitimidir. Derin öznitelik çıkarımı ve transfer öğrenme için 10 farklı önceden eğitilmiş ESA modelleri (AlexNet, ResNet50, DenseNet201, VGG16, VGG19, DarkNet53, ShuffleNet, Squeezenet, NASNetMobile ve MobileNetV2) kullanılmıştır. Derin özniteliklerin sınıflandırılması için Destek Vektör Makineleri (DVM) sınıflandırıcısı kullanılmıştır. İnce ayarlı MobileNetV2 modelinin başarısı, elde edilen tüm sonuçlar arasında en yüksek olan %99,25 doğruluk puanı üretmiştir. AlexNet modelinden çıkarılan derin özniteliklerin 10 kat çapraz doğrulama test başarısı %97,8 bulunurken, geliştirilen 21 katmanlı ESA modelinin uçtan uca eğitimi %94,25 sonuç vermiştir. Bu çalışmada kullanılan veri seti, Dicle Üniversitesi Tıp Fakültesi Göğüs Hastalıkları ve Tüberküloz kliniği ile yoğun bakım ünitesinden ve göğüs polikliniğinden elde edilen pnömonili ve sağlıklı CXR görüntülerinden oluşmaktadır.

Keywords

Pnömoni Evrişimsel Sinir Ağları Derin Öznitelik Çıkarımı Transfer Öğrenme Göğüs Röntgen Görüntüleri

References

  • Luján-García J. E., Yáñez-Márquez C., Villuendas-Rey Y., Camacho-Nieto O., A transfer learning method for pneumonia classification and visualization, Applied Sciences, 10 (8), 2908, 2020.
  • Mahomed N., Van Ginneken, B., Philipsen R. H., Melendez J., Moore D. P., Moodley H., Sewchuran T., Mathew D., Madhi S. A., Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children, Pediatric Radiology, 50 (4), 482-491,2020.
  • Kumar S., Singh P., Ranjan M., A review on deep learning-based pneumonia detection systems, International Conference on Artificial Intelligence and Smart Systems (ICAIS), India, 289-296,2021.
  • Doi K., Computer-aided diagnosis in medical imaging: historical review, current status and future potential, Computerized medical imaging and graphics, 31 (4-5), 198-211, 2007.
  • WHO. Standardization of Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children; World Health Organization: Geneva, Switzerland, 2001.
  • Ayan E., Ünver H. M., Diagnosis of pneumonia from chest X-ray images using deep learning, In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT),1-5,2019.
  • El Zein O. M., Soliman M. M., Elkholy A. K., Ghali N. I., Transfer Learning Based Model for Pneumonia Detection in Chest X-ray Images, International Journal of Intelligent Engineering and Systems, 14 (5), 56-66, 2021.
  • Rahman T., Chowdhury M. E., Khandakar A., Islam K. R., Islam K. F., Mahbub Z. B., Kashem S., Transfer learning with deep convolutional neural network for pneumonia detection using chest X-ray, Applied Sciences, 10 (9), 3233, 2020.
  • Yaseliani M., Hamadani A. Z., Maghsoodi A. I., Mosavi A., Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers, IEEE Access, 10, 62110-62128, 2022.
  • Kermany D. S., Goldbaum M., Cai W., Valentim C. C., Liang H., Baxter S. L., ... & Zhang, K., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172 (5), 1122-1131, 2018.
  • Mujahid M., Rustam F., Álvarez R., Luis Vidal Mazón J., Díez I. D. L. T., Ashraf I., Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network, Diagnostics, 12 (5), 1280, 2022.
  • Szepesi P., Szilágyi L., Detection of pneumonia using convolutional neural networks and deep learning, Biocybernetics and Biomedical Engineering, 42 (3), 1012-1022, 2022.
  • Qaimkhani F. M., Hussain M., Shiren Y., Xingfang J., Pneumonia Detection Using Deep Learning Methods, International Journal of Scientific Advances (IJSCIA), 3 (3), 489-493, 2022.
  • Al-Dulaimi D. S., Mahmoud A. G., Hassan N. M., Alkhayyat A., Majeed S. A., Development of Pneumonia Disease Detection Model Based on Deep Learning Algorithm, Wireless Communications and Mobile Computing, 2022.
  • Alshehri A., Alharbi B., Alharbi A., Pneumonia Detection from Chest X-ray Images Based on Sequential Model, International Journal of Computer Science & Network Security, 22 (4), 53-58,2022.
  • Kaur R. P., Sharma A., Singh I., Malhotra R., Deep Learning-Based Pneumonia Recognition from Chest X-Ray Images, International Journal of Performability Engineering, 18 (5), 380-386, 2022.
  • Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers R. M., Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 2097-2106, 2017.
  • Ayan E., Karabulut B., Ünver H. M., Diagnosis of pediatric pneumonia with ensemble of deep convolutional neural networks in chest x-ray images, Arabian Journal for Science and Engineering, 47(2), 2123-2139, 2022.
  • Sourab S. Y., Kabir M. A., A comparison of hybrid deep learning models for pneumonia diagnosis from chest radiograms, Sensors International, 3, 100-167, 2022.
  • Saha A. K., Rahman M., An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network, In Sentimental Analysis and Deep Learning, Springer, 59-68, 2022.
  • İnik Ö., Ülker E., Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri, Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6 (3), 85-104, 2017.
  • Mohamed O., Khalid E. A., Mohammed O., Brahim, A., Content-based image retrieval using convolutional neural networks, In First International Conference on Real Time Intelligent Systems, Springer, 463-476, 2017.
  • Krizhevsky A., Sutskever I., Hinton G. E., ImageNet classification with deep convolutional neural networks, In NIPS, 1106–1114, 2012.
  • Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2014.
  • He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 770-778, 2016.
  • Huang G., Liu Z., Van Der Maaten L., Weinberger K. Q., Densely connected convolutional networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 4700-4708, 2017.
  • Sandler M., Howard A., Zhu M., Zhmoginov A., Chen L. C., Mobilenetv2: Inverted residuals and linear bottlenecks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 4510-4520, 2018.
  • Zhang X., Zhou X., Lin M., Sun J., Shufflenet: An extremely efficient convolutional neural network for mobile devices, In Conference on Computer Vision and Pattern Recognition, 6848-6856, 2018.
  • Iandola F. N., Han S., Moskewicz M. W., Ashraf K., Dally W. J., Keutzer K., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size, arXiv preprint arXiv:1602.07360 ,2016.
  • Zoph B., Vasudevan V., Shlens J., Le Q. V., Learning transferable architectures for scalable image recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 8697-8710, 2018.
  • Redmon J., Farhadi A., Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018.
  • Gülgün O. D., Hamza E., Classification performance comparisons of deep learning models in pneumonia diagnosis using chest x-ray images, Turkish Journal of Engineering, 4 (3), 129-141, 2020.
  • Çallı E., Sogancioglu E., Van Ginneken B., Van Leeuwen K. G., Murphy K., Deep learning for chest X-ray analysis: A survey, Medical Image Analysis, 72, 102-125, 2021.
  • Glasmachers T., Limits of end-to-end learning, In Asian Conference on Machine Learning, Seul-Kore, 17-32, 2017.
  • Cortes C., Vapnik V., Support-vector networks, Machine learning, 20(3), 273-297,1995.
  • Karcioğlu A. A., Aydin T., Sentiment analysis of Turkish and english twitter feeds using Word2Vec model, In 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas-Türkiye,1-4, 2019.
  • Karcioğlu A. A., Bulut H., Performance Evaluation of Classification Algorithms Using Hyperparameter Optimization, In 2021 6th International Conference on Computer Science and Engineering (UBMK), Ankara-Türkiye, 354-358, 2021.
  • Nahzat S., Yağanoğlu M., Classification of Epileptic Seizure Dataset Using Different Machine Learning Algorithms and PCA Feature Reduction Technique, Journal of Investigations on Engineering and Technology, 4 (2), 47-60, 2021.
  • Taşcı E., Onan A., K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi, Akademik Bilişim, 1 (1), 4-18, 2016.
  • Cover T., Hart P., Nearest neighbor pattern classification, IEEE transactions on information theory, 13 (1), 21-27, 1967.
  • Breiman L., Random forests. Machine learning, 45 (1), 5-32, 2001.
  • Altay Y., Delialioğlu R. A., Diagnosing lameness with the Random Forest classification algorithm using thermal cameras and digital colour parameters, Mediterranean Agricultural Sciences, 35 (1), 47-54, 2022.
  • Türkoğlu M., Hanbay K., Sivrikaya, I. S., Hanbay, D., Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9 (1), 334-345, 2021.
  • Visuña L., Yang D., Garcia-Blas J., Carretero J., Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning, BMC Medical Imaging, 22 (1), 1-16,2022.
  • Yadav S., Shukla, S., Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification, In 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram-Hindistan, 78-83, 2016.

Year 2024, Volume: 39 Issue: 2, 729 - 740, 30.11.2023

Zehra Kadiroğlu Erkan Deniz Abdurrahman Şenyiğit

https://doi.org/10.17341/gazimmfd.1204092

Abstract

Keywords

Pnömoni Evrişimsel Sinir Ağları Derin Öznitelik Çıkarımı Transfer Öğrenme Göğüs Röntgen Görüntüleri

References

  • Luján-García J. E., Yáñez-Márquez C., Villuendas-Rey Y., Camacho-Nieto O., A transfer learning method for pneumonia classification and visualization, Applied Sciences, 10 (8), 2908, 2020.
  • Mahomed N., Van Ginneken, B., Philipsen R. H., Melendez J., Moore D. P., Moodley H., Sewchuran T., Mathew D., Madhi S. A., Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children, Pediatric Radiology, 50 (4), 482-491,2020.
  • Kumar S., Singh P., Ranjan M., A review on deep learning-based pneumonia detection systems, International Conference on Artificial Intelligence and Smart Systems (ICAIS), India, 289-296,2021.
  • Doi K., Computer-aided diagnosis in medical imaging: historical review, current status and future potential, Computerized medical imaging and graphics, 31 (4-5), 198-211, 2007.
  • WHO. Standardization of Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children; World Health Organization: Geneva, Switzerland, 2001.
  • Ayan E., Ünver H. M., Diagnosis of pneumonia from chest X-ray images using deep learning, In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT),1-5,2019.
  • El Zein O. M., Soliman M. M., Elkholy A. K., Ghali N. I., Transfer Learning Based Model for Pneumonia Detection in Chest X-ray Images, International Journal of Intelligent Engineering and Systems, 14 (5), 56-66, 2021.
  • Rahman T., Chowdhury M. E., Khandakar A., Islam K. R., Islam K. F., Mahbub Z. B., Kashem S., Transfer learning with deep convolutional neural network for pneumonia detection using chest X-ray, Applied Sciences, 10 (9), 3233, 2020.
  • Yaseliani M., Hamadani A. Z., Maghsoodi A. I., Mosavi A., Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers, IEEE Access, 10, 62110-62128, 2022.
  • Kermany D. S., Goldbaum M., Cai W., Valentim C. C., Liang H., Baxter S. L., ... & Zhang, K., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172 (5), 1122-1131, 2018.
  • Mujahid M., Rustam F., Álvarez R., Luis Vidal Mazón J., Díez I. D. L. T., Ashraf I., Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network, Diagnostics, 12 (5), 1280, 2022.
  • Szepesi P., Szilágyi L., Detection of pneumonia using convolutional neural networks and deep learning, Biocybernetics and Biomedical Engineering, 42 (3), 1012-1022, 2022.
  • Qaimkhani F. M., Hussain M., Shiren Y., Xingfang J., Pneumonia Detection Using Deep Learning Methods, International Journal of Scientific Advances (IJSCIA), 3 (3), 489-493, 2022.
  • Al-Dulaimi D. S., Mahmoud A. G., Hassan N. M., Alkhayyat A., Majeed S. A., Development of Pneumonia Disease Detection Model Based on Deep Learning Algorithm, Wireless Communications and Mobile Computing, 2022.
  • Alshehri A., Alharbi B., Alharbi A., Pneumonia Detection from Chest X-ray Images Based on Sequential Model, International Journal of Computer Science & Network Security, 22 (4), 53-58,2022.
  • Kaur R. P., Sharma A., Singh I., Malhotra R., Deep Learning-Based Pneumonia Recognition from Chest X-Ray Images, International Journal of Performability Engineering, 18 (5), 380-386, 2022.
  • Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers R. M., Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 2097-2106, 2017.
  • Ayan E., Karabulut B., Ünver H. M., Diagnosis of pediatric pneumonia with ensemble of deep convolutional neural networks in chest x-ray images, Arabian Journal for Science and Engineering, 47(2), 2123-2139, 2022.
  • Sourab S. Y., Kabir M. A., A comparison of hybrid deep learning models for pneumonia diagnosis from chest radiograms, Sensors International, 3, 100-167, 2022.
  • Saha A. K., Rahman M., An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network, In Sentimental Analysis and Deep Learning, Springer, 59-68, 2022.
  • İnik Ö., Ülker E., Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri, Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6 (3), 85-104, 2017.
  • Mohamed O., Khalid E. A., Mohammed O., Brahim, A., Content-based image retrieval using convolutional neural networks, In First International Conference on Real Time Intelligent Systems, Springer, 463-476, 2017.
  • Krizhevsky A., Sutskever I., Hinton G. E., ImageNet classification with deep convolutional neural networks, In NIPS, 1106–1114, 2012.
  • Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2014.
  • He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 770-778, 2016.
  • Huang G., Liu Z., Van Der Maaten L., Weinberger K. Q., Densely connected convolutional networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 4700-4708, 2017.
  • Sandler M., Howard A., Zhu M., Zhmoginov A., Chen L. C., Mobilenetv2: Inverted residuals and linear bottlenecks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 4510-4520, 2018.
  • Zhang X., Zhou X., Lin M., Sun J., Shufflenet: An extremely efficient convolutional neural network for mobile devices, In Conference on Computer Vision and Pattern Recognition, 6848-6856, 2018.
  • Iandola F. N., Han S., Moskewicz M. W., Ashraf K., Dally W. J., Keutzer K., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size, arXiv preprint arXiv:1602.07360 ,2016.
  • Zoph B., Vasudevan V., Shlens J., Le Q. V., Learning transferable architectures for scalable image recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, 8697-8710, 2018.
  • Redmon J., Farhadi A., Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018.
  • Gülgün O. D., Hamza E., Classification performance comparisons of deep learning models in pneumonia diagnosis using chest x-ray images, Turkish Journal of Engineering, 4 (3), 129-141, 2020.
  • Çallı E., Sogancioglu E., Van Ginneken B., Van Leeuwen K. G., Murphy K., Deep learning for chest X-ray analysis: A survey, Medical Image Analysis, 72, 102-125, 2021.
  • Glasmachers T., Limits of end-to-end learning, In Asian Conference on Machine Learning, Seul-Kore, 17-32, 2017.
  • Cortes C., Vapnik V., Support-vector networks, Machine learning, 20(3), 273-297,1995.
  • Karcioğlu A. A., Aydin T., Sentiment analysis of Turkish and english twitter feeds using Word2Vec model, In 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas-Türkiye,1-4, 2019.
  • Karcioğlu A. A., Bulut H., Performance Evaluation of Classification Algorithms Using Hyperparameter Optimization, In 2021 6th International Conference on Computer Science and Engineering (UBMK), Ankara-Türkiye, 354-358, 2021.
  • Nahzat S., Yağanoğlu M., Classification of Epileptic Seizure Dataset Using Different Machine Learning Algorithms and PCA Feature Reduction Technique, Journal of Investigations on Engineering and Technology, 4 (2), 47-60, 2021.
  • Taşcı E., Onan A., K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi, Akademik Bilişim, 1 (1), 4-18, 2016.
  • Cover T., Hart P., Nearest neighbor pattern classification, IEEE transactions on information theory, 13 (1), 21-27, 1967.
  • Breiman L., Random forests. Machine learning, 45 (1), 5-32, 2001.
  • Altay Y., Delialioğlu R. A., Diagnosing lameness with the Random Forest classification algorithm using thermal cameras and digital colour parameters, Mediterranean Agricultural Sciences, 35 (1), 47-54, 2022.
  • Türkoğlu M., Hanbay K., Sivrikaya, I. S., Hanbay, D., Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9 (1), 334-345, 2021.
  • Visuña L., Yang D., Garcia-Blas J., Carretero J., Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning, BMC Medical Imaging, 22 (1), 1-16,2022.
  • Yadav S., Shukla, S., Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification, In 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram-Hindistan, 78-83, 2016.
There are 45 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Zehra Kadiroğlu 0000-0002-2696-8138

Erkan Deniz 0000-0002-9048-6547

Abdurrahman Şenyiğit 0000-0001-9603-2231

Early Pub Date October 18, 2023
Publication Date November 30, 2023
Submission Date November 14, 2022
Acceptance Date April 12, 2023
Published in Issue Year 2024 Volume: 39 Issue: 2

Cite

APA Kadiroğlu, Z., Deniz, E., & Şenyiğit, A. (2023). Göğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(2), 729-740. https://doi.org/10.17341/gazimmfd.1204092
AMA Kadiroğlu Z, Deniz E, Şenyiğit A. Göğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılması. GUMMFD. November 2023;39(2):729-740. doi:10.17341/gazimmfd.1204092
Chicago Kadiroğlu, Zehra, Erkan Deniz, and Abdurrahman Şenyiğit. “Göğüs röntgen görüntülerinde pnömoni Tespiti için Derin öğrenme Modellerinin karşılaştırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, no. 2 (November 2023): 729-40. https://doi.org/10.17341/gazimmfd.1204092.
EndNote Kadiroğlu Z, Deniz E, Şenyiğit A (November 1, 2023) Göğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 2 729–740.
IEEE Z. Kadiroğlu, E. Deniz, and A. Şenyiğit, “Göğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılması”, GUMMFD, vol. 39, no. 2, pp. 729–740, 2023, doi: 10.17341/gazimmfd.1204092.
ISNAD Kadiroğlu, Zehra et al. “Göğüs röntgen görüntülerinde pnömoni Tespiti için Derin öğrenme Modellerinin karşılaştırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/2 (November 2023), 729-740. https://doi.org/10.17341/gazimmfd.1204092.
JAMA Kadiroğlu Z, Deniz E, Şenyiğit A. Göğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılması. GUMMFD. 2023;39:729–740.
MLA Kadiroğlu, Zehra et al. “Göğüs röntgen görüntülerinde pnömoni Tespiti için Derin öğrenme Modellerinin karşılaştırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 39, no. 2, 2023, pp. 729-40, doi:10.17341/gazimmfd.1204092.
Vancouver Kadiroğlu Z, Deniz E, Şenyiğit A. Göğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılması. GUMMFD. 2023;39(2):729-40.