ABSTRACT
The severe acute respiratory syndrome(SARS-CoV2) led to a pandemic of respiratory disease, namely COVID19. The disease has scaled worldwide and has become a global health concern. Unfortunately, the pandemic not just cost several individuals their lives but also, resulted in many people losing their jobs and life savings. In times like these, ordinary people become fearful of their resources in a world that gives its best resources to the wealthiest beings. Following the pandemic, the world suffered greatly and survival was rather difficult. As a result, numerous analytical techniques were developed to address this issue, with the key one being the discovery that the efficacy of clinically tested vaccines is actually quite poor. When researchers and medical professionals were unable to find a cure, radiologists and engineers created techniques to detect infected chests with the help of X-rays. Our proposed solution involves a CNN + LSTM model which has secured an accuracy of 98% compared to 95% of the trusted VGG-16 architecture. Our model's area under the curve (AUC) scores reached 99.458% while using RMSprop. A crucial feature of image processing till depth is accessible through scanning features from the layers of images using CNN. Our model uses 5 convolution blocks to detect the features. The coordination of activator functions, learning rates, and flattening has enabled accurate in-point predictions. With merely X-rays, models like ours ensure that anyone can easily detect covid-19. The best results obtained were at a learning rate =0.01 with RMSprop and Adam functions. The model has good fortune in detecting any other lung disease which occurs in the near future, as our data collectively rounds up to 4.5 gigabytes of data providing higher precision. © 2023 IEEE.
ABSTRACT
The World Health Organization has designated COVID-19 a pandemic because its emergence has influenced more than 50 million world's population. Around 14 million deaths have been reported worldwide from COVID-19. In this research work, we have presented a method for autonomous screening of COVID-19 and Pneumonia subjects from cough auscultation analysis. Deep learning-based model (MobileNet v2) is used to analyze a 6757 self-collected cough dataset. The experimentation has demonstrated the efficiency of the proposed technique in distinguishing between COVID-19 and Pneumonia. The results have demonstrated the cumulative accuracy of 99.98%, learning rate of 0.0005 and validation loss of 0.0028. Furthermore, cough analysis can be performed for other patients screening of other pulmonary abnormalities. © 2021 IEEE.
ABSTRACT
Due to the high incident rate of the novel corona virus popularly known as COVID-19, the number of suspected patients needing diagnosis presents overwhelming pressure on hospital and health management systems. This has led to global pandemic and eventual lockdown in many countries. More so, the infected patients present a higher risk of infecting the healthcare workers. This is because once a patient is positive of the virus, the recovery progress or deterioration needs to be monitored by medical experts and other health workers, which eventually exposes them to the infection. In this paper, we present an automatic prognosis of COVID-19 from a computed tomography (CT) scan using deep convolution neural networks (CNN). The models were trained using a super-convergence discriminative fine-tuning algorithm, which uses a layer-specific learning rate to fine-tune a deep CNN model;this learning rate is increased or decreased per iteration to avoid the saddle-point problem and achieve the best performance within few training epochs. The best performance results of our model were obtained as 98.57% accuracy, 98.59% precision and 98.55% recall rate. This work is therefore, presented to aid radiologist to safely and conveniently monitor the recovery of infected patients. © 2021 IEEE.