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2000
Volume 25, Issue 8
  • ISSN: 1871-5265
  • E-ISSN: 2212-3989

Abstract

Aims/Introduction

This research aims to develop an advanced deep-learning framework for detecting respiratory diseases, including COVID-19, pneumonia, and tuberculosis (TB), using chest X-ray scans.

Methods

A Deep Neural Network (DNN)-based system was developed to analyze medical images and extract key features from chest X-rays. The system leverages various DNN learning algorithms to study X-ray scan color, curve, and edge-based features. The Adam optimizer is employed to minimize error rates and enhance model training.

Results and Discussion

A dataset of 1800 chest X-ray images, consisting of COVID-19, pneumonia, TB, and typical cases, was evaluated across multiple DNN models. The highest accuracy was achieved using the VGG19 model. The proposed system demonstrated an accuracy of 94.72%, with a sensitivity of 92.73%, a specificity of 96.68%, and an F1-score of 94.66%. The error rate was 5.28% when trained with 80% of the dataset and tested on 20%. The VGG19 model showed significant accuracy improvements of 32.69%, 36.65%, 42.16%, and 8.1% over AlexNet, GoogleNet, InceptionV3, and VGG16, respectively. The prediction time was also remarkably low, ranging between 3 and 5 seconds.

Conclusion

The proposed deep learning model efficiently detects respiratory diseases, including COVID-19, pneumonia, and TB, within seconds. The method ensures high reliability and efficiency by optimizing feature extraction and maintaining system complexity, making it a valuable tool for clinicians in rapid disease diagnosis.

© 2025 Bentham Science Publishers
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2025-05-27
2026-01-05

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A Deep Neural Network Framework for the Detection of Bacterial Diseases from Chest X-Ray Scans
Infect. Disord. Drug Targets 25, E18715265358132 (2025); https://doi.org/10.2174/0118715265358132250429115426
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  • Article Type:     Research Article
Keyword(s): COVID-19; deep neural network; Pneumonia; respiratory diseases; tuberculosis; x-ray

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