RT Journal Article
T1 X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).
A1 Khan, Ali Yousuf
A1 Luque-Nieto, Miguel Ángel
A1 Saleem, Muhammad Imran
A1 Nava-Baro, Enrique
K1 COVID-19 - Diagnóstico
K1 Pulmones - Enfermedades
K1 Diagnóstico por imagen
AB On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently. Currently, the most widely used methodis Reverse Transcription Polymerase Chain Reaction (RT-PCR), a clinical technique for infection identification. However, RT-PCR is expensive, has limited sensitivity, and requires specialized medical expertise. One of the major challenges in the rapid diagnosis of COVID-19 is the need for reliable imaging, particularly X-ray imaging. This work takes advantage of artificial intelligence (AI) techniques to enhance diagnostic accuracy by automating the detection of COVID-19 infections from chest X-ray (CXR) images. We obtained and analyzed CXR images from the Kaggle public database (4035 images in total), including cases of COVID-19, viral pneumonia, pulmonary opacity, and healthy controls. By integrating advanced techniques with transfer learning from pre-trained convolutional neural networks (CNNs), specifically InceptionV3, ResNet50, and Xception, we achieved an accuracy of 95%, significantly higher than the 85.5% achieved with ResNet50 alone. Additionally, our proposed method, CXR-DNNs, can accurately distinguish between three different types of chest X-ray images for the first time. This computer-assisted diagnostic tool has the potential to significantly enhance the speed and accuracy of COVID-19 diagnoses.
PB MDPI
YR 2024
FD 2024-12-19
LK https://hdl.handle.net/10630/35959
UL https://hdl.handle.net/10630/35959
LA eng
NO Khan, A.Y.; Luque-Nieto, M.-A.; Saleem, M.I.; Nava-Baro, E. X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs). J. Imaging 2024, 10, 328. https://doi.org/10.3390/jimaging10120328
NO This research was funded by a grant (PCM-00006) from the Regional Government of Andalusia (Spain) through the project “CAMSUB3D: Advanced 3D camera for optimized underwater imaging and wireless charging” (Cod.25046, Complementary Plan for Marine Sciences and the Recovery, Transformation and Resilience Plan).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026