In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust feature learning ability. In particular, a modified residual network with asymmetric convolution and attention mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic and low-level detailed information. Experimental results on two public COVID-19 radiography databases have demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966 and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios. Consequently, one can conclude that this work has both practical value in providing reliable reference to the radiologist and theoretical significance in developing methods to build robust features with strong presentation ability. 

https://doi.org/10.1016/j.eswa.2022.118029

Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision

This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein that relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). The statistical analysis of patient profiles has shown a significant difference (p-value: 0.041) for ischemic heart disease between COVID-19 and healthy subjects. The Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35, p-value: <0.001). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

/pdf/detection-of-covid-19-in-smartphone-based-breathing-39xpr8xz.pdf

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.

Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images

Healthcare traditionally relies on single-modality approaches, which limit the information available for medical decisions. However, advancements in technology and the availability of diverse data sources have made it feasible to integrate multiple modalities and gain a more comprehensive understanding of patients' conditions. Multi-modality approaches involve fusing and analyzing various data types, including medical images, biosignals, clinical records, and other relevant sources. This systematic review provides a comprehensive exploration of the multi-modality approaches in healthcare, with a specific focus on disease diagnosis and prognosis. The adoption of multi-modality approaches in healthcare is crucial for personalized medicine, as it enables a comprehensive profile of each patient, considering their genetic makeup, imaging characteristics, clinical history, and other relevant factors. The review also discusses the technical challenges associated with fusing heterogeneous multimodal data and highlights the emergence of deep learning approaches as a powerful paradigm for multimodal data integration. 

Multi-Modality Approaches for Medical Support Systems: A Systematic Review of the Last Decade

COVID-19 has now been declared a ‘Global Pandemic’ by WHO. The pandemic has affected more than 200 countries since its first outbreak in December 2019. The spread of COVID-19 resulted in a state of lockdown globally. India too, closed its borders to contain the virus. Those worst affected by the pandemic are migrant workers at the ‘Bottom of Pyramid’ (BoP) due to unemployment and lack of monetary aid. Family sustenance has been difficult for them, with children impacted physically and psychologically. This paper proposes a ProductService System (PSS) that provides essential emergency kits to infants (6-12 months), children (1-6 years), and their mothers during such emergencies. This PSS scheme strives to fulfil their basic hygiene, nutritional and psychological requirements. Three types of kits are distributed to the migrant families using an online service platform. The entire system operates on a sustainable, single-use plastic-free design. The case study of this humanitarian scheme is specific to India but is also valid for other developing nations. Reaching out to the communities is achieved through a smartphone app and website. The system uses ICT infrastructure to connect various stakeholders and can be admirably adapted to the framework of an inclusive smart city.

/pdf/a-pandemic-specific-emergency-essentials-kit-for-children-in-10bs48dfa2.pdf

A Pandemic-specific ‘Emergency Essentials Kit’ for Children in the Migrant BoP communities

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A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.

A Pandemic-specific ‘Emergency Essentials Kit’ for Children in the Migrant BoP communities