# COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy
## 2 Related Work  
The novel coronavirus 2019 (COVID-2019), which initially appeared in the metropolis city of China in Dec 2019, unfold quickly around the world and has become a pandemic. It has caused a devastating impact on each daily life, public health, and also the international economy. It is crucial to discover positive cases as early as possible, and therefore, forestall the additional spread of this epidemic, in addition to quickly treating affected patients. The requirement for auxiliary diagnostic tools has been raised, as there are not any correct machine-controlled toolkits available. Recent findings by [PERSON] et al. (2019) obtained exploitation radiology imaging techniques counsel that such pictures contain salient data concerning the COVID-19 virus. Application of advanced computing (AI) techniques including imaging are often useful for the correct detection of this disease and may even be helpful to beat the matter of an absence of specialized physicians in remote villages. During this work, a novel model for automatic COVID-19 detection exploitation raw chest X-ray pictures is presented (in Supplementary Materials). Their planned model is developed to produce accurate nosology for binary classification (COVID vs. no-findings) and multi-class classification (COVID vs. no-findings vs. pneumonia). Their model made a classification accuracy of 98.08% for binary categories and 87.02% for multi-class cases. The DarkNet model was utilized in their study as a classifier for you simply look once (YOLO) real-time object detection system. They tended to enforce seventeen convolutional layers and introduced a different filtering on every layer. [PERSON] et al. [2] also researched using artificial intelligence to quickly diagnose patients who are positive for COVID-19, artificial intelligence algorithms will combine chest CT images with clinical symptoms, exposure history, and laboratory tests. A total of 419 (46.3 percent) of the 905 patients tested positive for SARS-CoV-2 using a real-time RT-PCR assay and next-generation sequencing RT-PCR. When compared to a senior thoracic radiologist, the AI system had an area under the curve of 0.92 and had identical sensitivity in a test set of 279 cases. The AI approach they applied also enhanced the detection of COVID-19 positive patients with normal CT scans who were positive by RT-PCR, correctly identifying 17 of 25 (68%) patients, whereas radiologists categorized all of these patients as COVID-19 negative. [PERSON] et al. [3] also stated in this paper--with the title AI4 COVID-19--that a cough is a symptom of approximately thirty medical illnesses that are unrelated to COVID-19. This makes diagnosing a COVID-19 infection just based on cough an extremely difficult interdisciplinary task. The team tackle this issue by comparing the pathomorphological changes in the respiratory system caused by COVID-19 infection to those caused by other respiratory illnesses. They use transfer learning to overcome the lack of COVID-19 cough training data. They use a multi-pronged mediator-focused risk-averse AI architecture to limit the danger of misdiagnosis caused by the problem's complicated dimensionality. AI4 COVID-19 can discriminate between COVID-19 coughs and numerous types of non-COVID-19 coughs, according to the findings. The accuracy is high enough to warrant a large-scale collection of labeled cough data to assess AI4 COVID-19's generalization capacity. AI4 COVID-19 is not a diagnostic tool for clinical use. Instead, it provides a screening tool that can be used by anybody, at anytime, anywhere. It can also be used as a clinical decision support tool to direct clinical testing and treatment to people who need it the most, resulting in more lives being saved. [PERSON] et.al [4] has developed the framework makes use of transfer learning, which allowed to train a neural network using a fraction of the data required by traditional methods.