Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques

Theodora Sanida (), Maria Vasiliki Sanida, Argyrios Sideris and Minas Dasygenis
Additional contact information
Theodora Sanida: Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece
Maria Vasiliki Sanida: Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece
Argyrios Sideris: Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece
Minas Dasygenis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece

J, 2024, vol. 7, issue 3, 1-17

Abstract: Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including recall, F1-score, accuracy, precision, and area under the curve (AUC). The results of the advanced VGG16 model showcased remarkable accuracy, achieving 98.58%. This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector.

Keywords: pulmonary diagnosis; CycleGAN; generative AI techniques; deep learning algorithms; lung diagnosis; chest X-ray imaging (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-8800/7/3/17/pdf (application/pdf)
https://www.mdpi.com/2571-8800/7/3/17/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjopen:v:7:y:2024:i:3:p:17-318:d:1455310

Access Statistics for this article

J is currently edited by Ms. Angelia Su

More articles in J from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().