Research Article
Enhancing Pneumonia Detection from Chest Radiographs Through a VGG-16-based Deep Learning Approach
Sourav Sana
,
Priyankar Biswas*,
A. T. M. Saiful Islam
Issue:
Volume 11, Issue 5, October 2025
Pages:
60-72
Received:
11 November 2025
Accepted:
26 November 2025
Published:
11 December 2025
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.
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 var...
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Research Article
Distribution of Dipper, Non-dipper, or Riser Phenotypes in Patients with Mild OSA Untreated with CPAP at UMAA No. 68 in Chihuahua, Mexico
Issue:
Volume 11, Issue 5, October 2025
Pages:
73-77
Received:
25 November 2025
Accepted:
11 December 2025
Published:
31 December 2025
DOI:
10.11648/j.ejcbs.20251105.12
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Abstract: "Obstructive sleep apnea (OSA) is associated with alterations in blood pressure (BP) regulation, influencing hypertension (HTN) control and increasing cardiovascular risk, with three nocturnal BP patterns described: a 10–20% decrease (“dipper”), a 0–10% decrease (“non-dipper”), or an increase during sleep (“riser”). This analytical cross-sectional observational study aimed to identify the distribution of dipper, non-dipper, and riser phenotypes in patients with untreated mild OSA evaluated at UMAA No. 68, Chihuahua. Patients with a diagnosis of mild OSA who were not receiving CPAP therapy were included, and data from 2021–2023 were obtained from the pulmonology service. Twenty-four–hour ambulatory blood pressure monitoring (ABPM) was performed, and SPSS v26 was used for statistical analysis. A total of 70 patients were analyzed (mean age 54.46 ± 15.9 years; 55.7% women), with hypertension documented in 31.4% of participants; overall, 82.9% exhibited a dipper phenotype and 17.1% a riser phenotype. The dipper phenotype predominated among patients with untreated mild OSA, while the riser phenotype was present in 17.1% of cases-approximately twice the prevalence reported in the general population-suggesting that nocturnal BP phenotype should be considered a relevant clinical feature in this population."
Abstract: "Obstructive sleep apnea (OSA) is associated with alterations in blood pressure (BP) regulation, influencing hypertension (HTN) control and increasing cardiovascular risk, with three nocturnal BP patterns described: a 10–20% decrease (“dipper”), a 0–10% decrease (“non-dipper”), or an increase during sleep (“riser”). This analytical cross-sectional ...
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