Abstract
A variety of viral infections can develop in pneumonia–known to be highly catastrophic lung disease. Due to the close association between pneumonia and other lung disorders, the diagnosis of pneumonia using chest X-ray images presents a significant challenge. Due to this issue, higher levels of accuracy cannot be gained from the recent approaches for detecting pneumonia. In this research, pneumonia is classified using deep learning algorithms. CNN model was developed to make chest X-ray diagnosis easier. Furthermore, the utilization of pre-trained convolutional neural network (CNN) models, which extract features from vast datasets, proves highly advantageous in the branch of image classification applications. In our analysis, we use a selection process to determine the most suitable CNN model for the task at hand. CNN models offer substantial assistance in the evaluation of chest X-ray images, particularly in the identification of pneumonia. To effectively identify pneumonic lungs in chest X-rays and contribute to pneumonia treatment, this article presents a range of convolutional neural network models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ortiz-Toro C, García-Pedrero A, Lillo-Saavedra M, Gonzalo-Martín C (2022) Automatic pneumonia detection in chest X-ray images using textural features. Comput Biol Med 145:105466
Wang L, Wang H, Huang Y, Yan B, Chang Z, Liu Z, Zhao M, Cui L, Song J, Li F (2022) Trends in the application of deep learning networks in medical image analysis: evolution between 2012 and 2020. Eur J Radiol 146:110069
Malhotra P, Gupta S, Koundal D, Zaguia A, Kaur M, Lee H-N (2022) Deep learning-based computer-aided pneumothorax detection using chest X-ray images. Sensors 22(2278):1–23
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K et al (2017) CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv:1711.05225 [cs.CV], arXiv:1711.05225v3 [cs.CV], https://doi.org/10.48550/arXiv.1711.05225
Stephen O, Sain M, Maduh UJ, Jeong D-U (2019) An efficient deep learning approach to pneumonia classification in healthcare. J Healthc Eng 2019(4180949):1–7
Atitallah SB, Driss M, Boulila W, Koubaa A, Ghézala HB (2022) Fusion of convolutional neural networks based on Dempster-Shafer theory for automatic pneumonia detection from chest X-ray images. Int J Imaging Syst Technol 32(2):658–672
Maselli G, Bertamino E, Capalbo C, Mancini R, Orsi GB, Napoli C, Napoli C (2021) Hierarchical convolutional models for automatic pneumonia diagnosis based on X-ray images: new strategies in public health. Ann IG 33(6):644–655
Ayan E, Karabulut B, Ünver HM (2022) Diagnosis of pediatric pneumonia with ensemble of deep convolutional neural networks in chest X-ray images. Arab J Sci Eng 47:2123–2139
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16×16 words: transformers for image recognition at scale. arXiv:2010.11929 [cs.CV], arXiv:2010.11929v2 [cs.CV], https://doi.org/10.48550/arXiv.2010.11929
Wu H, Xie P, Zhang H, Li D, Cheng M (2020) Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks. J Intell Fuzzy Syst 39(3):2893–2907
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jha, P., Rohilla, M., Goyal, A., Arora, S., Sharma, R., Kumar, J. (2024). A Comparative Analysis of Pneumonia Detection Using Chest X-rays with DNN. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 785. Springer, Singapore. https://doi.org/10.1007/978-981-99-6544-1_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-6544-1_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6543-4
Online ISBN: 978-981-99-6544-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)