A hybrid convolutional neural network model to detect

A hybrid convolutional neural network (CNN)-based model is proposed in the article for accurate detection of COVID-19, pneumonia, and normal patients using chest X-ray images. The input images are first pre-processed to tackle problems associated with the formation of the dataset from different sources, image quality issues, and imbalances in the dataset. The literature suggests that several abnormalities can be found with limited medical image datasets by using transfer learning. Hence, various pre-trained CNN models: VGG-19, InceptionV3, MobileNetV2, and DenseNet are adopted in the present work. Finally, with the help of these models, four hybrid models: VID (VGG-19, Inception, and DenseNet), VMI(VGG-19, MobileNet, and Inception), VMD (VGG-19, MobileNet, and DenseNet), and IMD(Inception, MobileNet, and DenseNet) are proposed. The model outcome is also tested using five-fold cross-validation. The best-performing hybrid model is the VMD model with an overall testing accuracy of 97.3%. Thus, a new hybrid model architecture is presented in the work that combines three individual base CNN models in a parallel configuration to counterbalance the shortcomings of individual models. The experimentation result reveals that the proposed hybrid model outperforms most of the previously suggested models. This model can also be used in the identification of diseases, especially in rural areas where limited laboratory facilities are available.

A hybrid convolutional neural network model to detect COVID‐19 and pneumonia using chest X‐ray images

A hybrid convolutional neural network (CNN)‐based model is proposed in the article for accurate detection of COVID‐19, pneumonia, and normal patients using chest X‐ray images. The input images are first pre‐processed to tackle problems associated with the formation of the dataset from different sources, image quality issues, and imbalances in the dataset. The literature suggests that several abnormalities can be found with limited medical image datasets by using transfer learning. Hence, various pre‐trained CNN models: VGG‐19, InceptionV3, MobileNetV2, and DenseNet are adopted in the present work. Finally, with the help of these models, four hybrid models: VID (VGG‐19, Inception, and DenseNet), VMI(VGG‐19, MobileNet, and Inception), VMD (VGG‐19, MobileNet, and DenseNet), and IMD(Inception, MobileNet, and DenseNet) are proposed. The model outcome is also tested using five‐fold cross‐validation. The best‐performing hybrid model is the VMD model with an overall testing accuracy of 97.3%. Thus, a new hybrid model architecture is presented in the work that combines three individual base CNN models in a parallel configuration to counterbalance the shortcomings of individual models. The experimentation result reveals that the proposed hybrid model outperforms most of the previously suggested models. This model can also be used in the identification of diseases, especially in rural areas where limited laboratory facilities are available. [ FROM AUTHOR]

Anxiety level among government employees admitted to dedicated COVID-19 hospital.

INTRODUCTION: Patients of COVID-19 patients while in a hospital may have stigma, fear, and guilt among them. However, the data on anxiety among the admitted COVID-19 patients are lacking in India and elsewhere. Hence, the study was conducted among the admitted patient of COVID-19 to describe their anxiety status. METHODS: The study was conducted as a cross-sectional study in a designated COVID-19 hospital in Delhi. The data were collected from October 22, 2020, to November 21, 2020. All patients who were admitted to the hospital for more than 72 h were eligible for participation. The data collection was done using a questionnaire. The questionnaire consists of two parts. One part was sociodemographic variables, and the other part was the Anxiety Scale. The anxiety score was collected on the Zung Self-Rating Anxiety Scale. RESULTS: A total of 132 eligible patients were admitted during the period. The questionnaire was answered by 122 (92.4%) patients. All patients were male. The patients' mean age was 33.5 years (standard deviation = 8.9 years), with a range of 21 years-65 years. The mean score of the Zung Self-Rating Scale was 29.5 (7.2), with an interquartile range of 24-33. There were only five patients (4.4%; 95% confidence interval: 1.3%-9.3%) whose scores were 45 or more, indicating mild-to-moderate anxiety. There was no statistically significant association between any sociodemographic variable and Anxiety Rating Scale. CONCLUSION: The anxiety level in the specialized population was low due to social security. The level of anxiety among health-care workers may be further explored.

Predictive models of COVID-19 in India: A rapid review.

BACKGROUND: The mathematical modelling of coronavirus disease-19 (COVID-19) pandemic has been attempted by a wide range of researchers from the very beginning of cases in India. Initial analysis of available models revealed large variations in scope, assumptions, predictions, course, effect of interventions, effect on health-care services, and so on. Thus, a rapid review was conducted for narrative synthesis and to assess correlation between predicted and actual values of cases in India. METHODS: A comprehensive, two-step search strategy was adopted, wherein the databases such as Medline, google scholar, MedRxiv, and BioRxiv were searched. Later, hand searching for the articles and contacting known modelers for unpublished models was resorted. The data from the included studies were extracted by the two investigators independently and checked by third researcher. RESULTS: Based on the literature search, 30 articles were included in this review. As narrative synthesis, data from the studies were summarized in terms of assumptions, model used, predictions, main recommendations, and findings. The Pearson's correlation coefficient (r) between predicted and actual values (n = 20) was 0.7 (p = 0.002) with R2 = 0.49. For Susceptible, Infected, Recovered (SIR) and its variant models (n = 16) 'r' was 0.65 (p = 0.02). The correlation for long-term predictions could not be assessed due to paucity of information. CONCLUSION: Review has shown the importance of assumptions and strong correlation between short-term projections but uncertainties for long-term predictions. Thus, short-term predictions may be revised as more and more data become available. The assumptions too need to expand and firm up as the pandemic evolves.