Exploring transfer learning in chest radiographic images within the interplay between Covid-19 and diabetes
Bibliographic Data
| ID | 22069764 |
|---|---|
| Authors | Muhammad Shoaib (0000-0001-9305-3766, CECOS University), Nasir Sayed (Islamia College University), Babar Shah (0000-0002-5090-4695, Zayed University), Tariq Hussain (0000-0001-5848-3840, Zhejiang Gongshang University), Ahmad Ali AlZubi (0000-0001-8477-8319, King Saud University), Sufian Ahmad AlZubi (Jordan University of Science and Technology), Farman Ali (0000-0002-9420-1588, Sungkyunkwan University, corresponding author) |
| Year | 2023 |
| Volume | 11 |
| Pages | 1297909-1297909 |
| Publication date | 2023-10-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1297909 |
| PMID | 37920574 |
| OpenAlex | W4387742057 |
| Language | EN |
| References cited | 29 |
The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group
Diabetes mellitus · Disease · Radiography · Radiology · Receiver operating characteristic · Transfer of learning · Computer Science · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI · Medicine · Phonocardiography and Auscultation Techniques · Artificial Intelligence · Internal Medicine
| Citation velocity | historical |
|---|---|
| Highly cited | No |