Abstract
Objective: This study aims to contribute to this gap by evaluating the performance of various deep learning models, including a proposed CNN model, ResNet50, and EfficientNetB0, for the detection of bacterial pneumonia from chest X-rays.
Material and methods: This study investigates the use of artificial intelligence (AI) in detecting pneumonia from chest X-ray (CXR) images using deep learning techniques, specifically Convolutional Neural Networks (CNN), ResNet50, and EfficientNetB0.
Results: A created novel dataset consisting of 1,228 images of bacterial pneumonia and 1,228 images of non-pneumonia cases, is used for model training and evaluation. X-ray images obtained from Yozgat Bozok Medical Faculty are classified by a specialist physician and supplemented with additional images from a publicly available dataset to eliminate class imbalance. Three deep learning models are implemented and evaluated in terms of accuracy, precision, recall, and F1-score. All models achieved an accuracy of 97%, with high performance in detecting both pneumonia and non-pneumonia cases. The Proposed CNN model showed precision and recall values of 1.00 and 0.94 for non-pneumonia and 0.95 and 1.00 for pneumonia detection, respectively. EfficientNetB0 and ResNet50 demonstrated similar robust performance.
Conclusion: The results indicate that AI-based models can offer reliable and accurate pneumonia detection, supporting clinical decision-making processes and acting as a valuable second opinion for physicians. These findings highlight the potential of AI in enhancing diagnostic accuracy and efficiency, particularly in resource-limited healthcare settings. Further validation with larger datasets and clinical trials is necessary to confirm the generalizability of these models for widespread clinical use.
References
- 1. Irfan A, Adivishnu AL, Sze-To A, Dehkharghanian T, Rahnamayan S, Tizhoosh HR. Classifying Pneumonia among Chest X-Rays Using Transfer Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:2186-9.
- 2. Liufu R, Chen Y, Wan XX, Liu RT, Jiang W, Wang C, et al. Sepsisinduced Coagulopathy: The Different Prognosis in Severe Pneumonia and Bacteremia Infection Patients. Clin Appl Thromb Hemost. 2023;29:10760296231219249.
- 3. Robba C, Battaglini D, Pelosi P, Rocco PRM. Multiple organ dysfunction in SARS-CoV-2: MODS-CoV-2. Expert Rev Respir Med. 2020;14(9):865-8.
- 4. Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J. The WHO estimates of excess mortality associated with the COVID-19 pandemic. Nature. 2023;613(7942):130-7.
- 5. Asselah T, Durantel D, Pasmant E, Lau G, Schinazi RF. COVID-19: Discovery, diagnostics and drug development. J Hepatol. 2021;74(1):168-84.
- 6. Salvatore C, Interlenghi M, Monti CB, Ippolito D, Capra D, Cozzi A, et al. Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. Diagnostics (Basel). 2021;11(3):530.
- 7. Khan MA, Azhar M, Ibrar K, Alqahtani A, Alsubai S, Binbusayyis A, et al. COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence. Comput Intell Neurosci. 2022;2022:4254631.
- 8. Kufel J, Bargieł K, Koźlik M, Czogalik Ł, Dudek P, Jaworski A, et al. Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review. Int J Med Sci. 2022;19(12):1743-52.
- 9. Gupta A, Sheth P, Xie P. Neural architecture search for pneumonia diagnosis from chest X-rays. Sci Rep. 2022;12(1):11309.
- 10. Anai S, Hisasue J, Takaki Y, Hara N. Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images. Can Respir J. 2022;2022:8026580.
- 11. Kermany D, Zhang K, Goldbaum M. "Labeled optical coherence tomography (oct) and chest x-ray images for classification." Mendeley data 2.2 (2018): 651.
- 12. Deng L, Dong Y. "Deep learning: methods and applications." Foundations and trends® in signal processing 7.3–4 (2014): 197-387.
- 13. Kelleher JD. Deep learning. Vol. 1. Cambridge, Massachusetts: MIT press; 2019:159-84.
- 14. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of 36th International Conference on Machine Learning, PMLR 97. 2019:6105-14.
- 15. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-8.
- 16. Sahin ME. "Image processing and machine learning‐based bone fracture detection and classification using X‐ray images." Int J Imag Syst Tech (2023);33:853-65.
- 17. Sahin ME. "Deep learning-based approach for detecting COVID-19 in chest X-rays." Bıomed Signal Proces (2022);78:103977.
- 18. Sahin ME, Ulutas H, Yüce E.. "A deep learning approach for detecting pneumonia in chest X-rays." Avrupa Bilim ve Teknoloji Dergisi (2021);28:562-7.
Abstract
Amaç: Bu çalışma, akciğer röntgeninden bakteriyel pnömoninin saptanması için önerilen CNN modeli ResNet50 ve EfficientNetB0 dahil olmak üzere çeşitli derin öğrenme modellerinin performansını değerlendirerek bu boşluğa katkıda bulunmayı amaçlamaktadır.
Gereç ve yöntemler: Bu çalışma, özellikle Evrişimsel Sinir Ağları (CNN), ResNet50 ve EfficientNetB0 olmak üzere derin öğrenme tekniklerini kullanarak akciğer röntgeni görüntülerinden pnömoniyi tespit etmede yapay zekanın kullanımını araştırıyor.
Bulgular: Model eğitimi ve değerlendirmesi için 1.228 bakteriyel pnömoni görüntüsü ve 1.228 pnömoni olmayan vaka görüntüsünden oluşan oluşturulmuş yeni bir veri seti kullanıldı. Yozgat Bozok Tıp Fakültesi'nden alınan röntgen görüntüleri, uzman hekim tarafından sınıflandırılıp, sınıf dengesizliğini ortadan kaldırmak amacıyla kamuya açık bir veri setinden alınan ek görüntülerle desteklendi. Üç derin öğrenme modeli uygulandı ve doğruluk, kesinlik, geri çağırma ve F1 puanı açısından değerlendirildi. Tüm modeller hem pnömoni hem de pnömoni dışı vakaların tespitinde yüksek performansla %97'lik bir doğruluğa ulaştı. Önerilen CNN modeli, pnömoni dışı için sırasıyla 1,00 ve 0,94 ve pnömoni tespiti için 0,95 ve 1,00 hassasiyet ve geri çağırma değerleri gösterdi. EfficientNetB0 ve ResNet50 benzer güçlü performans sergiledi.
Sonuç: Sonuçlar, yapay zeka tabanlı modellerin güvenilir ve doğru pnömoni tespiti sunabileceğini, klinik karar verme süreçlerini destekleyebileceğini ve hekimler için değerli bir ikinci görüş olarak hareket edebileceğini gösteriyor. Bu bulgular, yapay zekanın özellikle kaynakların sınırlı olduğu sağlık hizmetlerinde teşhis doğruluğunu ve verimliliğini artırma potansiyelini vurgulamaktadır. Bu modellerin yaygın klinik kullanıma yönelik genelleştirilebilirliğini doğrulamak için daha büyük veri kümeleri ve klinik çalışmalarla daha fazla doğrulama yapılması gerekmektedir.
References
- 1. Irfan A, Adivishnu AL, Sze-To A, Dehkharghanian T, Rahnamayan S, Tizhoosh HR. Classifying Pneumonia among Chest X-Rays Using Transfer Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:2186-9.
- 2. Liufu R, Chen Y, Wan XX, Liu RT, Jiang W, Wang C, et al. Sepsisinduced Coagulopathy: The Different Prognosis in Severe Pneumonia and Bacteremia Infection Patients. Clin Appl Thromb Hemost. 2023;29:10760296231219249.
- 3. Robba C, Battaglini D, Pelosi P, Rocco PRM. Multiple organ dysfunction in SARS-CoV-2: MODS-CoV-2. Expert Rev Respir Med. 2020;14(9):865-8.
- 4. Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J. The WHO estimates of excess mortality associated with the COVID-19 pandemic. Nature. 2023;613(7942):130-7.
- 5. Asselah T, Durantel D, Pasmant E, Lau G, Schinazi RF. COVID-19: Discovery, diagnostics and drug development. J Hepatol. 2021;74(1):168-84.
- 6. Salvatore C, Interlenghi M, Monti CB, Ippolito D, Capra D, Cozzi A, et al. Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. Diagnostics (Basel). 2021;11(3):530.
- 7. Khan MA, Azhar M, Ibrar K, Alqahtani A, Alsubai S, Binbusayyis A, et al. COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence. Comput Intell Neurosci. 2022;2022:4254631.
- 8. Kufel J, Bargieł K, Koźlik M, Czogalik Ł, Dudek P, Jaworski A, et al. Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review. Int J Med Sci. 2022;19(12):1743-52.
- 9. Gupta A, Sheth P, Xie P. Neural architecture search for pneumonia diagnosis from chest X-rays. Sci Rep. 2022;12(1):11309.
- 10. Anai S, Hisasue J, Takaki Y, Hara N. Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images. Can Respir J. 2022;2022:8026580.
- 11. Kermany D, Zhang K, Goldbaum M. "Labeled optical coherence tomography (oct) and chest x-ray images for classification." Mendeley data 2.2 (2018): 651.
- 12. Deng L, Dong Y. "Deep learning: methods and applications." Foundations and trends® in signal processing 7.3–4 (2014): 197-387.
- 13. Kelleher JD. Deep learning. Vol. 1. Cambridge, Massachusetts: MIT press; 2019:159-84.
- 14. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of 36th International Conference on Machine Learning, PMLR 97. 2019:6105-14.
- 15. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-8.
- 16. Sahin ME. "Image processing and machine learning‐based bone fracture detection and classification using X‐ray images." Int J Imag Syst Tech (2023);33:853-65.
- 17. Sahin ME. "Deep learning-based approach for detecting COVID-19 in chest X-rays." Bıomed Signal Proces (2022);78:103977.
- 18. Sahin ME, Ulutas H, Yüce E.. "A deep learning approach for detecting pneumonia in chest X-rays." Avrupa Bilim ve Teknoloji Dergisi (2021);28:562-7.
Details
| Primary Language | English |
|---|---|
| Subjects | Chest Diseases |
| Journal Section | Original Research |
| Authors | |
| Publication Date | June 15, 2025 |
| Submission Date | November 28, 2024 |
| Acceptance Date | March 4, 2025 |
| Published in Issue | Year 2025 Volume: 15 Issue: 2 |
