CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays
This paper describes the development of a CNN model for the analysis of chest X-rays and the automated diagnosis of pneumonia, bacterial or viral, and lung pathologies resulting from COVID-19, offering new insights for further research through the development of an AI-based diagnostic tool, which ca...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/7/186 |
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| author | Cristian Randieri Andrea Perrotta Adriano Puglisi Maria Grazia Bocci Christian Napoli |
| author_facet | Cristian Randieri Andrea Perrotta Adriano Puglisi Maria Grazia Bocci Christian Napoli |
| author_sort | Cristian Randieri |
| collection | DOAJ |
| description | This paper describes the development of a CNN model for the analysis of chest X-rays and the automated diagnosis of pneumonia, bacterial or viral, and lung pathologies resulting from COVID-19, offering new insights for further research through the development of an AI-based diagnostic tool, which can be automatically implemented and made available for rapid differentiation between normal pneumonia and COVID-19 starting from X-ray images. The model developed in this work is capable of performing three-class classification, achieving 97.48% accuracy in distinguishing chest X-rays affected by COVID-19 from other pneumonias (bacterial or viral) and from cases defined as normal, i.e., without any obvious pathology. The novelty of our study is represented not only by the quality of the results obtained in terms of accuracy but, above all, by the reduced complexity of the model in terms of parameters and a shorter inference time compared to other models currently found in the literature. The excellent trade-off between the accuracy and computational complexity of our model allows for easy implementation on numerous embedded hardware platforms, such as FPGAs, for the creation of new diagnostic tools to support medical practice. |
| format | Article |
| id | doaj-art-6020f14f033f4ac7a7b71cf269c2b14f |
| institution | DOAJ |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-6020f14f033f4ac7a7b71cf269c2b14f2025-08-20T03:13:42ZengMDPI AGBig Data and Cognitive Computing2504-22892025-07-019718610.3390/bdcc9070186CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-RaysCristian Randieri0Andrea Perrotta1Adriano Puglisi2Maria Grazia Bocci3Christian Napoli4Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, ItalyDepartment of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, ItalyClinical and Research Department, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, Via Portuense, 292, 00149 Rome, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, ItalyThis paper describes the development of a CNN model for the analysis of chest X-rays and the automated diagnosis of pneumonia, bacterial or viral, and lung pathologies resulting from COVID-19, offering new insights for further research through the development of an AI-based diagnostic tool, which can be automatically implemented and made available for rapid differentiation between normal pneumonia and COVID-19 starting from X-ray images. The model developed in this work is capable of performing three-class classification, achieving 97.48% accuracy in distinguishing chest X-rays affected by COVID-19 from other pneumonias (bacterial or viral) and from cases defined as normal, i.e., without any obvious pathology. The novelty of our study is represented not only by the quality of the results obtained in terms of accuracy but, above all, by the reduced complexity of the model in terms of parameters and a shorter inference time compared to other models currently found in the literature. The excellent trade-off between the accuracy and computational complexity of our model allows for easy implementation on numerous embedded hardware platforms, such as FPGAs, for the creation of new diagnostic tools to support medical practice.https://www.mdpi.com/2504-2289/9/7/186COVID-19CNNpneumoniamedical imagingAI-assisted diagnostics |
| spellingShingle | Cristian Randieri Andrea Perrotta Adriano Puglisi Maria Grazia Bocci Christian Napoli CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays Big Data and Cognitive Computing COVID-19 CNN pneumonia medical imaging AI-assisted diagnostics |
| title | CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays |
| title_full | CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays |
| title_fullStr | CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays |
| title_full_unstemmed | CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays |
| title_short | CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays |
| title_sort | cnn based framework for classifying covid 19 pneumonia and normal chest x rays |
| topic | COVID-19 CNN pneumonia medical imaging AI-assisted diagnostics |
| url | https://www.mdpi.com/2504-2289/9/7/186 |
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