Enhancing COVID-19 Diagnosis through Deep Learning on a coupled Edge-Cloud Computing technique using CT Scan Images

The present research looks at how deep learning models may improve the accuracy of COVID-19 diagnosis using CT scan pictures. The project aims to improve the detection of COVID-19-specific patterns and features by using the capabilities of the VGG19, DenseNet201, and VGG16 models. The training and assessment were based on a collection of 2,521 CT scan images from patients with cardiac problems as well as those who tested positive for COVID-19. To assess the models' diagnostic abilities, performance metrics such as accuracy, precision, recall, and specificity are used. DenseNet201 regularly beats the competition across all key measures, revealing its potential for accurate COVID-19 diagnosis. The suggested edge-cloud architecture further integrates the models into real clinical scenarios. Given the important role of false negatives and false positives in medical diagnostics, the results highlight the need of looking at more than just accuracy. These models are useful diagnostic tools, but they should not be used in place of hands-on medical treatment. This study adds to the increasing body of knowledge at the interface of artificial intelligence and healthcare by providing insights into how deep learning models may be used to improve COVID-19 diagnostic accuracy and perhaps modify medical practices. To fully realize the promise of these findings in enhancing patient care and healthcare systems, more cooperation between machine learning researchers and healthcare practitioners is required.