DEEP LEARNING FOR HEALTHCARE APPLICATIONS

The COVID19 pandemic is an existential global health emergency affecting the entire planet. Countless individuals have died as a result of COVID, which is still present and spreading in nations including the United States, India, and Russia. Positive cases are increasing despite the fact that there are currently multiple vaccines available. So that a suspected COVID-19 patient can be saved, there is an urgent need for quick infection diagnosis with good visualisation. Results from procedures like Polymerase chain reaction (PCR) and Lateral flow tests (LFTs), which must be sent to a laboratory for examination, may not be available to patients for a few days. The outcomes, though, aren’t always accurate. Despite CT scan images being a common imaging modality among previously accessible, generally available, and less priced materials, deep learning algorithms have achieved progressive performance in computer-aided medical diagnosis. In order to discriminate between COVID-19 and non-COVID 19 in CT scan images, our study will use a range of deep learning techniques. The developed models’ respective levels of accuracy for diagnosing COVID-19 were 85.34%, 87.46%, and 88.15%. COVID-19 automated diagnostic using CT scan pictures is a quick and effective method for COVID-19 screening that doctors can use.