i-manager Publications

Pneumonia Detection using CNN: A Deep Learning Approach

Sri Hari Nallamala*, Yashwanthini Polireddy**, Venkata Naga Thanujeswary Peram***, Devika Narra****, Paavana Krishna Teja Maddali*****, Hari Naga Kiran Botlagunta******

*-****** Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India.

Periodicity:January - March'2025
DOI : https://doi.org/10.26634/jit.14.1.21936

Abstract

Pneumonia is a highly contagious lung infection characterized by inflammation of air sacs in one or both the lungs. The air sacs get filled with fluid resulting in fever, cough and difficult breathing. Chest X-ray images are used to detect pneumonia. The manual identification of pneumonia using chest X-ray images is typically time-consuming and prone to errors, which may delay diagnosis and treatment. So, a deep learning model is used for detecting the pneumonia without any delays. A Convolutional Neural Network is a type of deep learning model specifically designed for processing images. Two Convolutional Neural Network models, EfficientNet-B0 and VGG16, are trained. The model that gives the best accuracy is chosen as the final model. The VGG16 model provides an accuracy value of 89.90% and EfficientNet- B0 model provides an accuracy of 91.51%. This research outcome exhibits that EfficientNet-B0 provides better performance than VGG16. And the study indicates that the CNN model EfficientNet-B0 can make pneumonia detection more accurate and reliable, helping doctors in their diagnosis and treatment.

Keywords

Pneumonia Detection, Deep Learning, Chest X-ray, EfficientNet-B0, VGG16.

How to Cite this Article?

Nallamala, S. H., Polireddy, Y., Peram, V. N. T., Narra, D., Maddali, P. K. T., and Botlagunta, H. N. K. (2025). Pneumonia Detection using CNN: A Deep Learning Approach. i-manager’s Journal on Information Technology, 14(1), 17-25. https://doi.org/10.26634/jit.14.1.21936

References

[1]. Akbar, W., Soomro, A., Hussain, A., Hussain, T., Ali, F., Haq, M. I. U., & Alsagri, R. (2024). Pneumonia detection: A comprehensive study of diverse neural network architectures using chest X-rays. International Journal of Applied Mathematics and Computer Science, 34(4), 679-699.

[2]. Ali, M., Shahroz, M., Akram, U., Mushtaq, M. F., Altamiranda, S. C., Obregon, S. A., & Ashraf, I. (2024). Pneumonia detection using chest radiographs with novel efficientnetv2l model. IEEE Access, 12, 34691 - 34707.

[3]. Baghdadi, N., Maklad, A. S., Malki, A., & Deif, M. A. (2022). Reliable sarcoidosis detection using chest x-rays with efficientnets and stain-normalization techniques. Sensors, 22(10), 3846.

[4]. El Zein, O. M., Soliman, M. M., Elkholy, A. K., & Ghali, N. I. (2021). Transfer learning based model for pneumonia detection in chest X-ray images. International Journal of Intelligent Engineering and Systems, 14(5), 56-66.

[5]. GM, H., Gourisaria, M. K., Rautaray, S. S., & Pandey, Manjusha. (2021). Pneumonia detection using CNN through chest X-ray. Journal of Engineering Science and Technology (JESTEC), 16(1), 861-876.

[6]. Gunwant, H., Joshi, A., Sharma, M., & Gupta, D. (2022). Automated medical diagnosis and classification of skin diseases using efficinetnet-B0 convolutional neural network. In New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics (pp. 3-19). Springer International Publishing.

[7]. Hua, K. L., Hsu, C. H., Hidayati, S. C., Cheng, W. H., & Chen, Y. J. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and Therapy, 8, 2015- 2022.

[8]. Humayun, M., Khalil, M. I., Alwakid, G., & Jhanjhi, N. Z. (2022). Superlative feature selection based image classification using deep learning in medical imaging. Journal of Healthcare Engineering, 2022(1), 7028717.

[9]. Hussain, A., Amin, S. U., Lee, H., Khan, A., Khan, N. F., & Seo, S. (2023). An automated chest X-ray image analysis for covid-19 and pneumonia diagnosis using deep ensemble strategy. IEEE Access, 11, 97207-97220.

[10]. Isl0am, M. T., Aowal, M. A., Minhaz, A. T., & Ashraf, K. (2017). Abnormality detection and localization in chest x- rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850.

[11]. Ismail, A. R., Nisa, S. Q., Shaharuddin, S. A., Masni, S. I., & Amin, S. A. S. (2024). Utilising VGG-16 of convolutional neural network for medical image classification. International Journal on Perceptive and Cognitive Computing, 10(1), 113-118.

[12]. Jiang, Z. P., Liu, Y. Y., Shao, Z. E., & Huang, K. W. (2021). An improved VGG16 model for pneumonia image classification. Applied Sciences, 11(23), 11185.

[13]. Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., & Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image- based deep learning. Cell, 172(5), 1122-1131.

[14]. Kumar, A., Nelson, L., & Arumugam, D. (2024). Deep learning-based classification of brain tumours on MRI images using EfficientNet-B0. In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 219-225). IEEE.

[15]. Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574-582.

[16]. Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71.

[17]. Nijhawan, R., Verma, R., Bhushan, S., Dua, R., & Mittal, A. (2017). An integrated deep learning framework approach for nail disease identification. In 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 197-202). IEEE.

[18]. Rahman, T., Chowdhury, M. E., Khandakar, A., Islam, K. R., Islam, K. F., Mahbub, Z. B., & Kashem, S. (2020). Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Applied Sciences, 10(9), 3233.

[19]. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., & Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 211-252.

[20]. Sharif Razavian, A., Azizpour, H., Sullivan, J., & Carlsson, S. (2014). CNN features off-the-shelf: An astounding baseline for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 806-813).

[21]. Sharma, S., & Guleria, K. (2023). A deep learning based model for the detection of pneumonia from chest X-ray images using VGG-16 and neural networks. Procedia Computer Science, 218, 357-366.

[22]. Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (pp. 6105-6114). PMLR.

[23]. Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019). Pneumonia detection using CNN based feature extraction. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-7).

[24]. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). 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 (pp. 2097-2106).

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	15	15	200	15
Pdf & Online	35	35	400	25

Pneumonia Detection using CNN: A Deep Learning Approach

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: