Covid-19 Classification on Chest X-ray Images Using Deep Learning Methods
Bibliographic Data
| ID | 15468467 |
|---|---|
| Authors | Marios Constantinou (0000-0002-6646-5877, Ionian University), Themis P Exarchos (0000-0001-9420-9419, Ionian University, corresponding author), Themis Exarchos (Ionian University), Aristidis G Vrahatis (0000-0003-1892-0000, Ionian University), Panagiotis Vlamos (0000-0003-0053-7847, Ionian University) |
| Year | 2023 |
| Volume | 20 |
| Issue | 3 |
| Pages | 2035-2035 |
| Publication date | 2023-01-22 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph20032035 |
| PMID | 36767399 |
| OpenAlex | W4317812149 |
| Language | EN |
| References cited | 29 |
Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-ray images. The scope of this study is to highlight the significance and potential of individual deep learning models in COVID-19 CXR images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data that was not used for training or validation, authenticating their performance and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall and Accuracy, respectively. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19
Convolutional neural network · Coronavirus disease 2019 (COVID-19 · Deep learning · Disease · Infectious disease (medical specialty · Machine learning · Pathology · Perspective (graphical · Radiography · Radiology · Recall · Transfer of learning · Computer Science · COVID-19 diagnosis using AI · Medicine · Psychology · Radiology practices and education · Radiomics and Machine Learning in Medical Imaging · Artificial Intelligence
| Highly cited | No |
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