Pneumonia Detection and Analysis Using AlexNet
Research Article
Open Access
CC BY

Pneumonia Detection and Analysis Using AlexNet

Hanle Gao 1*
1 Heilongjiang University, Heilongjiang, China
*Corresponding author: 488737298@qq.com
Published on 2 October 2025
Journal Cover
ACE Vol.190
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-411-3
ISBN (Online): 978-1-80590-412-0
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Abstract

Because pneumonia incidence remains high and traditional diagnostic methods face efficiency bottlenecks, and since convolutional neural networks are increasingly applied in medical image analysis, this paper employs the AlexNet model to analyze chest X-ray images for pneumonia detection. The study optimizes the training process by tuning the number of epochs to identify the model with the best accuracy. Experimental results show that the model achieved an accuracy of 0.8108 (81.08%), demonstrating good capability for recognizing pneumonia in X-ray images. This method can help reduce the bias and time required by manual interpretation, effectively improve the efficiency of pneumonia screening, and gain valuable time for timely diagnosis and treatment.

Keywords:

Convolutional neural networks, Pneumonia, Medical image processing, AlexNet

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Gao,H. (2025). Pneumonia Detection and Analysis Using AlexNet. Applied and Computational Engineering,190,1-7.

References

[1]. Hao, K., Hu, L., & Ding, X. (2022). Weighted Fisher convolutional neural network model for breast cancer detection. Computer Technology and Development, 32(6), 179–185.

[2]. Shen, X., Zhang, X., Wang, Q., et al. (2023). Salivary gland tumor ultrasound image classification based on convolutional neural networks. Journal of Clinical Ultrasound in Medicine, 25(10), 849–855. https: //doi.org/10.16245/j.cnki.issn1008-6978.2023.10.020

[3]. Song, X., Wang, F., & Gao, J. (2024). Sickle cell image classification model based on CNN and KNN. Journal of Mudanjiang Normal University (Natural Science Edition), (4), 22–26. https: //doi.org/10.13815/j.cnki.jmtc(ns).2024.04.013

[4]. Shao, R., Liu, J., Ma, J., et al. (2024). Review of the application of convolutional neural networks in liver cancer pathological image diagnosis. Computer Systems & Applications, 33(4), 26–38. https: //doi.org/10.15888/j.cnki.csa.009466

[5]. Liu, P., An, D., & Feng, Y. (2024). Brain tumor image segmentation based on asymmetric U-shaped convolutional neural networks. Computer Systems & Applications, 33(8), 196–204. https: //doi.org/10.15888/j.cnki.csa.009613

[6]. Wu, X., Yang, X., Li, Z., et al. (2024). Brain tumor segmentation based on a combination of U-Net and watershed algorithms. Computer and Digital Engineering, 52(9), 2764–2770.

[7]. Cao, Q., Wang, F., & Niu, J. (2020). Optimization of brain tumor medical image segmentation based on 3D convolutional neural networks. Modern Electronic Technology, 43(3), 74–77. https: //doi.org/10.16652/j.issn.1004-373x.2020.03.018

[8]. Xu, L., Li, H., Liu, Z., et al. (2024). MRI image segmentation algorithm for brain gliomas based on 3D-Ghost convolutional neural networks: 3D-GA-Unet. Journal of Computer Applications, 44(4), 1294–1302.

[9]. Chen, F., Li, M., Jiang, Y., et al. (2024). Automatic recognition and segmentation of pancreas and tumors using deep convolutional network models: Based on 3D V-Net. Journal of Molecular Imaging, 47(11), 1170–1175.

[10]. Pei, G., Zhang, S., Zhang, J., et al. (2024). ECG-UNet: A lightweight medical image segmentation algorithm based on a U-shaped structure. Journal of Applied Science, 42(6), 922–933.

[11]. Zhang, T., Zhou, D., Li, J., et al. (2024). Convolutional network for fuzzy medical image edge detection. Computer Systems & Applications, 33(2), 198–206. https: //doi.org/10.15888/j.cnki.csa.009384

[12]. Xie, D., Li, L., & Miao, C. (2021). Handwritten digit recognition based on improved AlexNet convolutional neural network. Journal of Hebei University of Engineering (Natural Science Edition), 38(4), 102–106.

[13]. Tian, Y. (2025). Development and application of convolutional neural network models. Digital Communication World, (4), 108–110.

[14]. Peng, W., Ye, Y., & Qiu, X. (2025). Clinical characteristics of pneumonia patients with different severity levels. Great Doctor, 10(5), 99–102.

References

[1]. Hao, K., Hu, L., & Ding, X. (2022). Weighted Fisher convolutional neural network model for breast cancer detection. Computer Technology and Development, 32(6), 179–185.

[2]. Shen, X., Zhang, X., Wang, Q., et al. (2023). Salivary gland tumor ultrasound image classification based on convolutional neural networks. Journal of Clinical Ultrasound in Medicine, 25(10), 849–855. https: //doi.org/10.16245/j.cnki.issn1008-6978.2023.10.020

[3]. Song, X., Wang, F., & Gao, J. (2024). Sickle cell image classification model based on CNN and KNN. Journal of Mudanjiang Normal University (Natural Science Edition), (4), 22–26. https: //doi.org/10.13815/j.cnki.jmtc(ns).2024.04.013

[4]. Shao, R., Liu, J., Ma, J., et al. (2024). Review of the application of convolutional neural networks in liver cancer pathological image diagnosis. Computer Systems & Applications, 33(4), 26–38. https: //doi.org/10.15888/j.cnki.csa.009466

[5]. Liu, P., An, D., & Feng, Y. (2024). Brain tumor image segmentation based on asymmetric U-shaped convolutional neural networks. Computer Systems & Applications, 33(8), 196–204. https: //doi.org/10.15888/j.cnki.csa.009613

[6]. Wu, X., Yang, X., Li, Z., et al. (2024). Brain tumor segmentation based on a combination of U-Net and watershed algorithms. Computer and Digital Engineering, 52(9), 2764–2770.

[7]. Cao, Q., Wang, F., & Niu, J. (2020). Optimization of brain tumor medical image segmentation based on 3D convolutional neural networks. Modern Electronic Technology, 43(3), 74–77. https: //doi.org/10.16652/j.issn.1004-373x.2020.03.018

[8]. Xu, L., Li, H., Liu, Z., et al. (2024). MRI image segmentation algorithm for brain gliomas based on 3D-Ghost convolutional neural networks: 3D-GA-Unet. Journal of Computer Applications, 44(4), 1294–1302.

[9]. Chen, F., Li, M., Jiang, Y., et al. (2024). Automatic recognition and segmentation of pancreas and tumors using deep convolutional network models: Based on 3D V-Net. Journal of Molecular Imaging, 47(11), 1170–1175.

[10]. Pei, G., Zhang, S., Zhang, J., et al. (2024). ECG-UNet: A lightweight medical image segmentation algorithm based on a U-shaped structure. Journal of Applied Science, 42(6), 922–933.

[11]. Zhang, T., Zhou, D., Li, J., et al. (2024). Convolutional network for fuzzy medical image edge detection. Computer Systems & Applications, 33(2), 198–206. https: //doi.org/10.15888/j.cnki.csa.009384

[12]. Xie, D., Li, L., & Miao, C. (2021). Handwritten digit recognition based on improved AlexNet convolutional neural network. Journal of Hebei University of Engineering (Natural Science Edition), 38(4), 102–106.

[13]. Tian, Y. (2025). Development and application of convolutional neural network models. Digital Communication World, (4), 108–110.

[14]. Peng, W., Ye, Y., & Qiu, X. (2025). Clinical characteristics of pneumonia patients with different severity levels. Great Doctor, 10(5), 99–102.

Cite this article

Gao,H. (2025). Pneumonia Detection and Analysis Using AlexNet. Applied and Computational Engineering,190,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-411-3(Print) / 978-1-80590-412-0(Online)
Editor: Marwan Omar, Guozheng Rao
Conference website: https://www.confspml.org/tianjin.html
Conference date: 21 December 2025
Series: Applied and Computational Engineering
Volume number: Vol.190
ISSN: 2755-2721(Print) / 2755-273X(Online)

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