A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging

Lung diseases, including pneumonia and COVID-19, are prevalent globally, necessitating early diagnosis for effective treatment. Medical imaging is widely regarded as an effective method for detecting lung diseases. Numerous researchers have dedicated their efforts to developing advanced detection te...

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Main Author: Samia Dardouri
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Pulmonary Medicine
Online Access:http://dx.doi.org/10.1155/pm/6614016
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author Samia Dardouri
author_facet Samia Dardouri
author_sort Samia Dardouri
collection DOAJ
description Lung diseases, including pneumonia and COVID-19, are prevalent globally, necessitating early diagnosis for effective treatment. Medical imaging is widely regarded as an effective method for detecting lung diseases. Numerous researchers have dedicated their efforts to developing advanced detection techniques, significantly contributing to the prevention and management of these conditions. Despite advancements in imaging diagnostic methods, chest radiographs remain pivotal due to their cost-effectiveness and rapid results. This study proposes an automated system for detecting multiple lung diseases in x-ray and CT scans using a customized convolutional neural network (CNN) alongside pretrained models and an image enhancement approach. The dataset used comprises 6400 images sourced from Kaggle, categorized into three classes: pneumonia, COVID-19, and normal. To address dataset imbalance, data augmentation techniques were applied. The model includes preprocessing and classification stages, achieving high performance metrics: 96% precision, 95.33% recall, 95.66% F1-score, and 97.24% accuracy, highlighting its effectiveness compared to other deep learning models.
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spelling doaj-art-e14c9f46a1d14d6b96c5d7dd6c96f80e2025-07-02T00:00:41ZengWileyPulmonary Medicine2090-18442025-01-01202510.1155/pm/6614016A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray ImagingSamia Dardouri0Department of Computer ScienceLung diseases, including pneumonia and COVID-19, are prevalent globally, necessitating early diagnosis for effective treatment. Medical imaging is widely regarded as an effective method for detecting lung diseases. Numerous researchers have dedicated their efforts to developing advanced detection techniques, significantly contributing to the prevention and management of these conditions. Despite advancements in imaging diagnostic methods, chest radiographs remain pivotal due to their cost-effectiveness and rapid results. This study proposes an automated system for detecting multiple lung diseases in x-ray and CT scans using a customized convolutional neural network (CNN) alongside pretrained models and an image enhancement approach. The dataset used comprises 6400 images sourced from Kaggle, categorized into three classes: pneumonia, COVID-19, and normal. To address dataset imbalance, data augmentation techniques were applied. The model includes preprocessing and classification stages, achieving high performance metrics: 96% precision, 95.33% recall, 95.66% F1-score, and 97.24% accuracy, highlighting its effectiveness compared to other deep learning models.http://dx.doi.org/10.1155/pm/6614016
spellingShingle Samia Dardouri
A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging
Pulmonary Medicine
title A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging
title_full A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging
title_fullStr A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging
title_full_unstemmed A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging
title_short A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging
title_sort deep convolutional neural network model for lung disease detection using chest x ray imaging
url http://dx.doi.org/10.1155/pm/6614016
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