Deep Learning-Based Diagnosis of Pneumonia Using Convolutional Neural Networks

Ayesha Karunaratna Mudiyanselage

doi:10.57159/gadl.jcmm.3.3.240126

Authors

Ayesha Karunaratna Mudiyanselage University of Oulu, Oulu, North Ostrobothnia, Finland, 8000

DOI:

https://doi.org/10.57159/gadl.jcmm.3.3.240126

Keywords:

Pneumonia Diagnosis, Deep Learning, Chest X-rays, Convolutional Neural Network, Medical Imaging

Abstract

Pneumonia is a respiratory illness characterized by lung inflammation, often caused by pathogens such as viruses, bacteria, or fungi. Timely detection of pneumonia is crucial for effective treatment. While chest X-rays are commonly used for diagnosis, manual interpretation can be time-consuming, especially in areas with limited access to trained radiologists. Recently, deep learning models have emerged as an efficient method for pneumonia diagnosis. Many researchers are dedicated to enhancing the capabilities of pneumonia diagnosis through artificial intelligence methods. This study employs a convolutional neural network (CNN) for pneumonia diagnosis using an X-rays dataset from healthy individuals and those affected by pneumonia. The model achieved an accuracy of 59.9%, a precision of 77.75%, a recall of 59.9%, and an F1 score of 52.21% on the test dataset. Further tuning of the model’s hyperparameters is necessary to improve performance metrics.

References

Welte, Torres, and D. Nathwani, “Clinical and economic burden of community-acquired pneumonia among adults in Europe,” Thorax, vol. 67, no. 1, pp. 71–79, 2012, BMJ Publishing Group Ltd.

A. Depeursinge, A. S. Chin, A. N. Leung, D. Terrone, M. Bristow, G. Rosen, and D. L. Rubin, “Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution computed tomography,” Investigative Radiology, vol. 50, no. 4, pp. 261–267, 2015, LWW.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, et al., “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, no. 5, pp. 1122–1131, 2018, Elsevier.

S. Bhattacharjee, H.-G. Park, C.-H. Kim, D. Prakash, N. Madusanka, J.-H. So, N.-H. Cho, and H.-K. Choi, “Quantitative analysis of benign and malignant tumors in histopathology: Predicting prostate cancer grading using SVM,” Applied Sciences, vol. 9, 2019.

J. Wang, H. Zhu, S.-H. Wang, and Y.-D. Zhang, “A review of deep learning on medical image analysis,” Mobile Networks and Applications, vol. 26, no. 1, pp. 351–380, 2021.

M. Puttagunta and S. Ravi, “Medical image analysis based on deep learning approach,” Multimedia Tools and Applications, vol. 80, no. 16, pp. 24365–24398, 2021.

L. Fang and X. Wang, “COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images,” Biocybernetics and Biomedical Engineering, vol. 42, no. 3, pp. 977–994, 2022, Elsevier.

R. B. Nair and C. Kurian, “Pneumonia detection using convolutional neural network,” in Proceedings of National Seminar on Artificial Intelligence & Machine Learning, 2021, pp. 15.

R. Kundu, R. Das, Z. W. Geem, G.-T. Han, and R. Sarkar, “Pneumonia detection in chest X-ray images using an ensemble of deep learning models,” PLOS ONE, vol. 16, no. 9, pp. e0256630, 2021, Public Library of Science.

G. L. E. Maquen-Niño, J. G. Nuñez-Fernandez, F. Y. Taquila-Calderon, I. Adrianzén-Olano, P. De-La-Cruz-VdV, and G. Carrión-Barco, “Classification model using transfer learning for the detection of pneumonia in chest X-ray images,” International Journal of Online & Biomedical Engineering, vol. 20, no. 5, 2024.

M. Lakshmi, R. Das, and B. Manohar, “A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets,” Evolving Systems, pp. 1–20, 2024, Springer.

M. F. Hashmi, S. Katiyar, A. G. Keskar, N. D. Bokde, and Z. W. Geem, “Efficient pneumonia detection in chest X-ray images using deep transfer learning,” Diagnostics, vol. 10, no. 6, pp. 417, 2020, MDPI.

Q. An, W. Chen, and W. Shao, “A deep convolutional neural network for pneumonia detection in X-ray images with attention ensemble,” Diagnostics, vol. 14, no. 4, pp. 390, 2024, MDPI.