Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application

The predictors of long-COVID in the cohort of Turkish Thoracic Society- TURCOVID multicenter registry: One year follow-up results

Objective: To evaluate long-term effects of COVID-19, and to determine the risk factors in long-COVID in a cohort of the Turkish Thoracic Society (TTS)-TURCOVID multicenter registry.Methods: Thirteen centers participated with 831 patients; 504 patients were enrolled after exclusions. The study was designed in three-steps: (1) Phone questionnaire; (2) retrospective evaluation of the medical records; (3) face-to-face visit. Results: In the first step, 93.5% of the patients were hospitalized; 61.7% had a history of pneumonia at the time of diagnosis. A total of 27.1% reported clinical symptoms at the end of the first year. Dyspnea (17.00%), fatigue (6.30%), and weakness (5.00%) were the most prevalent long-term symptoms. The incidence of long-term symptoms was increased by 2.91 fold (95% CI 1.04-8.13, P=0.041) in the presence of chronic obstructive pulmonary disease and by 1.84 fold (95% CI 1.10-3.10, P=0.021) in the presence of pneumonia at initial diagnosis, 3.92 fold (95% Cl 2.29-6.72, P=0.001) of dyspnea and 1.69 fold (95% Cl 1.02-2.80, P=0.040) fatigue persists in the early-post-treatment period and 2.88 fold (95% Cl 1.52- 5.46, P=0.001) in the presence of emergency service admission in the post COVID period. In step 2, retrospective analysis of 231 patients revealed that 1.4% of the chest X-rays had not significantly improved at the end of the first year, while computed tomography (CT) scan detected fibrosis in 3.4%. In step 3, 138 (27.4%) patients admitted to face-to-face visit at the end of first year; at least one symptom persisted in 49.27% patients. The most common symptoms were dyspnea (27.60%), psychiatric symptoms (18.10%), and fatigue (17.40%). Thorax CT revealed fibrosis in 2.4% patients. Conclusions: COVID-19 symptoms can last for extended lengths of time, and severity of the disease as well as the presence of comorbidities might contribute to increased risk. Long-term clinical issues should be regularly evaluated after COVID-19

COVID-19: vaccination vs. hospitalization

Objective Vaccination is the most efficient way to control the coronavirus disease 2019 (COVID-19) pandemic, but vaccination rates remain below the target level in most countries. This multicenter study aimed to evaluate the vaccination status of hospitalized patients and compare two different booster vaccine protocols. Setting Inoculation in Turkey began in mid-January 2021. Sinovac was the only available vaccine until April 2021, when BioNTech was added. At the beginning of July 2021, the government offered a third booster dose to healthcare workers and people aged > 50 years who had received the two doses of Sinovac. Of the participants who received a booster, most chose BioNTech as the third dose. Methods We collected data from 25 hospitals in 16 cities. Patients hospitalized between August 1 and 10, 2021, were included and categorized into eight groups according to their vaccination status. Results We identified 1401 patients, of which 529 (37.7%) were admitted to intensive care units. Nearly half (47.8%) of the patients were not vaccinated, and those with two doses of Sinovac formed the second largest group (32.9%). Hospitalizations were lower in the group which received 2 doses of Sinovac and a booster dose of BioNTech than in the group which received 3 doses of Sinovac. Conclusion Effective vaccinations decreased COVID-19-related hospitalizations. The efficacy after two doses of Sinovac may decrease over time; however, it may be enhanced by adding a booster dose. Moreover, unvaccinated patients may be persuaded to undergo vaccination