Pneumonia is a significant respiratory disease with high global burdens, especially in resource limited settings where access to specialized radiology is restricted. Early and reliable diagnosis is essential for effective clinical intervention, yet manual interpretation of chest X-ray images is often time-consuming and subject to inter-observer variability. This framework employs deep learning for automated pneumonia detection using chest X-ray images, leveraging transfer learning with a pre-trained VGG-16 model and a custom DNN classifier that incorporates batch normalization and dropout layers to ensure stable training and prevent overfitting. The model achieved an accuracy of 92.79%, precision of 94.12%, recall of 94.36%, an F1-score of 94.24%, and an AUC of 0.98 on the public Chest X-Ray images (Pneumonia) dataset published on Kaggle, outperforming several state-of-the-art CNN methods. These performance metrics indicate that the proposed method exceeds several existing convolutional neural network-based techniques reported in contemporary studies. To enhance clinical transparency, Gradient weighted Class Activation Mapping (Grad-CAM) was utilized to visualize salient regions contributing to the model’s predictions, thereby improving interpretability and supporting potential clinical adoption. The results demonstrate that the framework is effective, computationally efficient, and capable of providing reliable diagnostic support. Its design makes it suitable for integration into real-time clinical decision support systems and telemedicine platforms, particularly in low-resource healthcare environments where rapid and accurate diagnostic tools are urgently needed.
| Published in | European Journal of Clinical and Biomedical Sciences (Volume 11, Issue 5) |
| DOI | 10.11648/j.ejcbs.20251105.11 |
| Page(s) | 60-72 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Pneumonia Detection, Chest X-rays, Transfer Learning, VGG-16, Deep Neural Networks, Medical Imaging
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APA Style
Sana, S., Biswas, P., Islam, A. T. M. S. (2025). Enhancing Pneumonia Detection from Chest Radiographs Through a VGG-16-based Deep Learning Approach. European Journal of Clinical and Biomedical Sciences, 11(5), 60-72. https://doi.org/10.11648/j.ejcbs.20251105.11
ACS Style
Sana, S.; Biswas, P.; Islam, A. T. M. S. Enhancing Pneumonia Detection from Chest Radiographs Through a VGG-16-based Deep Learning Approach. Eur. J. Clin. Biomed. Sci. 2025, 11(5), 60-72. doi: 10.11648/j.ejcbs.20251105.11
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Download@article{10.11648/j.ejcbs.20251105.11,
author = {Sourav Sana and Priyankar Biswas and A. T. M. Saiful Islam},
title = {Enhancing Pneumonia Detection from Chest Radiographs Through a VGG-16-based Deep Learning Approach},
journal = {European Journal of Clinical and Biomedical Sciences},
volume = {11},
number = {5},
pages = {60-72},
doi = {10.11648/j.ejcbs.20251105.11},
url = {https://doi.org/10.11648/j.ejcbs.20251105.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ejcbs.20251105.11},
abstract = {Pneumonia is a significant respiratory disease with high global burdens, especially in resource limited settings where access to specialized radiology is restricted. Early and reliable diagnosis is essential for effective clinical intervention, yet manual interpretation of chest X-ray images is often time-consuming and subject to inter-observer variability. This framework employs deep learning for automated pneumonia detection using chest X-ray images, leveraging transfer learning with a pre-trained VGG-16 model and a custom DNN classifier that incorporates batch normalization and dropout layers to ensure stable training and prevent overfitting. The model achieved an accuracy of 92.79%, precision of 94.12%, recall of 94.36%, an F1-score of 94.24%, and an AUC of 0.98 on the public Chest X-Ray images (Pneumonia) dataset published on Kaggle, outperforming several state-of-the-art CNN methods. These performance metrics indicate that the proposed method exceeds several existing convolutional neural network-based techniques reported in contemporary studies. To enhance clinical transparency, Gradient weighted Class Activation Mapping (Grad-CAM) was utilized to visualize salient regions contributing to the model’s predictions, thereby improving interpretability and supporting potential clinical adoption. The results demonstrate that the framework is effective, computationally efficient, and capable of providing reliable diagnostic support. Its design makes it suitable for integration into real-time clinical decision support systems and telemedicine platforms, particularly in low-resource healthcare environments where rapid and accurate diagnostic tools are urgently needed.},
year = {2025}
}
TY - JOUR T1 - Enhancing Pneumonia Detection from Chest Radiographs Through a VGG-16-based Deep Learning Approach AU - Sourav Sana AU - Priyankar Biswas AU - A. T. M. Saiful Islam Y1 - 2025/12/11 PY - 2025 N1 - https://doi.org/10.11648/j.ejcbs.20251105.11 DO - 10.11648/j.ejcbs.20251105.11 T2 - European Journal of Clinical and Biomedical Sciences JF - European Journal of Clinical and Biomedical Sciences JO - European Journal of Clinical and Biomedical Sciences SP - 60 EP - 72 PB - Science Publishing Group SN - 2575-5005 UR - https://doi.org/10.11648/j.ejcbs.20251105.11 AB - Pneumonia is a significant respiratory disease with high global burdens, especially in resource limited settings where access to specialized radiology is restricted. Early and reliable diagnosis is essential for effective clinical intervention, yet manual interpretation of chest X-ray images is often time-consuming and subject to inter-observer variability. This framework employs deep learning for automated pneumonia detection using chest X-ray images, leveraging transfer learning with a pre-trained VGG-16 model and a custom DNN classifier that incorporates batch normalization and dropout layers to ensure stable training and prevent overfitting. The model achieved an accuracy of 92.79%, precision of 94.12%, recall of 94.36%, an F1-score of 94.24%, and an AUC of 0.98 on the public Chest X-Ray images (Pneumonia) dataset published on Kaggle, outperforming several state-of-the-art CNN methods. These performance metrics indicate that the proposed method exceeds several existing convolutional neural network-based techniques reported in contemporary studies. To enhance clinical transparency, Gradient weighted Class Activation Mapping (Grad-CAM) was utilized to visualize salient regions contributing to the model’s predictions, thereby improving interpretability and supporting potential clinical adoption. The results demonstrate that the framework is effective, computationally efficient, and capable of providing reliable diagnostic support. Its design makes it suitable for integration into real-time clinical decision support systems and telemedicine platforms, particularly in low-resource healthcare environments where rapid and accurate diagnostic tools are urgently needed. VL - 11 IS - 5 ER -
Department of Electrical and Electronic Engineering, Gopalganj Science and Technology University (GSTU), Gopalganj, Bangladesh
Department of Electrical and Electronic Engineering, Gopalganj Science and Technology University (GSTU), Gopalganj, Bangladesh
Department of Electrical and Electronic Engineering, Gopalganj Science and Technology University (GSTU), Gopalganj, Bangladesh
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