A convolutional neural network for chest X-ray image classification
DOI:
https://doi.org/10.17721/2706-9699.2025.2.06Keywords:
convolutional neural network, cross-validation, chest X-ray images, classificationAbstract
This paper addresses the design of a convolutional neural network architecture for processing chest X-ray images using pattern recognition methods in the context of classification into the following classes: COVID-19 viral pneumonia, non-COVID pneumonia, and absence of disease. The development of a convolutional neural network architecture is a key component of technologies for timely and accurate diagnosis of lung diseases. In this work, a CNN architecture consisting of five convolutional layers separated by pooling layers is proposed. The network was trained using a batch size of 32 and the Adam optimization algorithm, achieving an overall classification accuracy of 94%.
References
Pashko A.O., Suchkov V.I. Application of convolutional neural networks to classification of lung X-ray images. XIII International Scientific and Practical Conference "Glushkov Readings. Modern Cybernetics", Kyiv, 2024.12.06. P. 235–239
Narin A., Kaya C., Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications. 2021, Vol. 24, Issue 3. P. 1207–1220. https://doi.org/10.1007/s10044-021-00984-y
Cohen J.P., Morrison P., Dao L., Roth K., Duong T., Ghassem M. COVID-19 Image Data Collection: Prospective Predictions are the Future. Journal of Machine Learning for Biomedical Imaging. 2020, Vol. 1. P. 1–38. https://doi.org/10.59275/j.melba.2020-48g7
Mooney P. Chest X-ray Images (Pneumonia) 2020. https://kaggle.com/datasets/paultimothymooney/chestxray-pneumonia
Khan A.I., Shah J.L., Bhat M.M. CoroNet: A Deep Neural Network for Detection and Diagnosis of COVID-19 from Chest X-ray Images. 2020.
Chowdhury A., Zhang H. Radiography Image Classification Using Deep Convolutional Neural Networks. Journal of Computer and Communications. 2024, Vol. 12. P. 199–209. https://doi.org/10.4236/jcc.2024.126012
Gour M., Jain S. Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images. arXiv preprint arXiv:2006.13817, 2020. https://arxiv.org/abs/2006.13817
Wang L., Wong A. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images. IEEE Access. 2020, Vol. 8. P. 85982–85993. https://doi.org/10.1109/ACCESS.2020.2995900
Xu X., Jiang X., Ma C., Du P., Li X., Lv S. et al. A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia. Engineering. 2020, Vol. 6, Issue 10. P. 1122–1129. https://doi.org/10.1016/j.eng.2020.04.010
Amuda K., Wakili A., Amoo T., Agbetu L., Wang Q., Feng J. Detecting SARSCoV-2 in CT Scans Using Vision Transformer and Graph Neural Network. Algorithms. 2025, Vol. 18, Issue 7. P. 413. https://doi.org/10.3390/a18070413
Gonzalez R.C., Woods R.E. Digital Image Processing. 4th ed. Pearson, 2018.
Kingma D.P., Ba J. Adam: A Method for Stochastic Optimization. 2014. https://doi.org/10.48550/arXiv.1412.6980
Prechelt L. Early Stopping — But When? In: Neural Networks: Tricks of the Trade. Springer, 1998. https://doi.org/10.1007/3-540-49430-8_3
