Comparative Analysis of COVID 19 Detection from Cough Speech Using Machine Learning Classifiers

Corona Virus Disease-2019, or COVID-19, has been on the rise since its emergence, so its early detection is necessary to stop it from spreading rapidly. Speech detection is one of the best ways to detect it at an early stage as it exhibits variations in the nasopharyngeal cavity and can be performed ubiquitously. In this research, three standard databases are used for detection of COVID-19 from speech signal. The feature set includes the baseline perceptual features such as spectral centroid, spectral crest, spectral decrease, spectral entropy, spectral flatness, spectral flux, spectral kurtosis, spectral roll off point, spectral skewness, spectral slope, spectral spread, harmonic to noise ratio, and pitch. 05 ML based classification techniques have been employed using these features. It has been observed that Generalized Additive Model (GAM) classifier offers an average of 95% and a maximum of 97.55% accuracy for COVID-19 detection from cough signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mitigating Information Interruptions by COVID-19 Face Masks: A Three-Stage Speech Enhancement Scheme

The coronavirus disease 2019 (COVID-19) preventive measures have resulted in significant lifestyle changes. One of the COVID-19 new normal is the usage of face masks for protection against airborne aerosol which creates distractions and interruptions in voice communication. It has a different influence on speech than the standard concept of noise affecting speech communication. Furthermore, it has varied effects on speech in different frequency bands. To provide a solution to this problem, a three-stage adaptive speech enhancement (SE) scheme is developed in this article. In the first stage, the tunable <inline-formula> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula>-factor wavelet transform (TQWT) features are extracted by properly setting the quality factor values and the number of levels from the input speech signal. In the second stage, the adjustable parameters of the preemphasis filter and modified multiband spectral subtraction (MBSS) are determined using bio-inspired techniques for different masking and signal-to-noise ratio (SNR) conditions. In the third stage, the weights, center values, standard deviation of the Gaussian radial basis functions, and input patterns of the radial basis function neural networks (RBFNNs) are updated to predict the optimized parameters from the input TQWT-based cepstral features (TQCFs). In the end, the performance of the proposed algorithm is compared with the standard SE algorithms using two speech datasets. IEEE

Insights into the SARS-CoV-2 Diagnosis in India: Present Status and Future Prospects

COVID-19, the infectious pandemic disease is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly disease was unknown before its catastrophic outbreak of the infection in Wuhan city of China, in December 2019. The pandemic situation has increased the demand of rapid enhancement of the in-vitro diagnostic assays which would enable the mass screening and testing. Several molecular and serological diagnostics assays such as direct viral antigen tests, nucleic acid amplification tests and serological tests were developed. Nucleic acid tests such as RT-PCR. TrueNAT, Feluda Test, loop-mediated isothermal amplification (LAMP) etc. detect the presence of RNA virus in the nasal or throat swab or from saliva. Antigen tests detect the presence of a virus as the antigen, which is a surface protein. Antibody tests such as enzyme-linked immunosorbent assays (ELISA), lateral flow assays (LFA), chemiluminescence assays (CLIA) etc. detect the presence of antibodies generated against SARS-CoV-2 in the blood samples.

CovidDiagnosis: Deep Diagnosis of COVID-19 Patients Using Chest X-Rays

As the COVID-19 pandemic threatens to overwhelm healthcare systems across the world, there is a need for reducing the burden on medical staff via automated systems for patient screening. Given the limited availability of testing kits with long turn-around test times and the exponentially increasing number of COVID-19 positive cases, X-rays offer an additional cheap and fast modality for screening COVID-19 positive patients, especially for patients exhibiting respiratory symptoms. In this paper, we propose a solution based on a combination of deep learning and radiomic features for assisting radiologists during the diagnosis of COVID-19. The proposed system of CovidDiagnosis takes a chest X-ray image and passes it through a pipeline comprising of a model for lung isolation, followed by classification of the lung regions into four disease classes, namely Healthy, Pneumonia, Tuberculosis and COVID-19. To assist our classification framework, we employ embeddings of disease symptoms produced by the CheXNet network by creating an ensemble. The proposed approach gives remarkable classification results on publicly available datasets of chest X-rays. Additionally, the system produces visualization maps which highlight the symptoms responsible for producing the classification decisions. This provides trustworthy and interpretable decisions to radiologists for the clinical deployment of CovidDiagnosis. Further, we calibrate our network using temperature scaling to give confidence scores which are representative of true correctness likelihood. © 2020, Springer Nature Switzerland AG.