Diagnosis of COVID-19 from CT Images and Respiratory Sound Signals Using Deep Learning Strategies

COVID-19 has been a major issue among various countries, and it has already affected millions of people across the world and caused nearly 4 million deaths. Various precautionary measures should be taken to bring the cases under control, and the easiest way for diagnosing the diseases should also be identified. An accurate analysis of CT has to be done for the treatment of COVID-19 infection, and this process is complex and it needs much attention from the specialist. It is also proved that the covid infection can be identified with the breathing sounds of the patient. A new framework was proposed for diagnosing COVID-19 using CT images and breathing sounds. The entire network is designed to predict the class as normal, COVID-19, bacterial pneumonia, and viral pneumonia using the multiclass classification network MLP. The proposed framework has two modules: (i) respiratory sound analysis framework and (ii) CT image analysis framework. These modules exhibit the workflow for data gathering, data preprocessing, and the development of the deep learning model (deep CNN + MLP). In respiratory sound analysis framework, the gathered audio signals are converted to spectrogram video using FFT analyzer. Features like MFCCs, ZCR, log energies, and Kurtosis are needed to be extracted for identifying dry/wet coughs, variability present in the signal, prevalence of higher amplitudes, and for increasing the performance in audio classification. All these features are extracted with the deep CNN architecture with the series of convolution, pooling, and ReLU (rectified linear unit) layers. Finally, the classification is done with a multilayer perceptron (MLP) classifier. In parallel to this, the diagnosis of the disease is improved by analyzing the CT images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Influence of social, physical & antenatal stresses in perinatal women of tertiary health care setup during COVID-19 pandemic

Corona Virus-19 had a detrimental effect on people's ability to live a healthy and active lifestyle. Social life is impacted by this outbreak, resulting in anxiety and psychosomatic illness. The purpose of this study is to determine the effect of social, physical, and psychological factors on perinatal women enrolled in tertiary health care facilities established during the COVID-19 pandemic. At Saifai' tertiary level hospital, a crosssectional observational study was conducted. A questionnaire calibrated on the Likert scale was administered to 72 inpatient perinatal mothers to assess their levels of anxiety (n=04), social network dependency (n=03), and quality of life (n=02). Physical characteristics were derived from baseline data. Each section's scores were added together. Group C has the highest mean anxiety level, followed by Groups D, A, and B. When compared using one-way ANOVA, the results are statistically significant at p<0.05. Group C and D have the highest mean of social network dependency and quality of life. The anxiety levels and quality of life in groups V (10.165.90) and N (20.073.28) were determined using an independent t-test with a significance level of p<0.05. According to the findings of the above study, during the COVID-19 pandemic, a lack of knowledge and education about COVID and pregnancy-related procedures can increase anxiety levels among perinatal women admitted to tertiary care facilities. It is recommended to conduct the study on a larger sample size in order to validate and ascertain the result's accuracy. © 2022 The authors.