Prediction of COVID-19 & Pneumonia using Machine Learning & Deep Learning Model

As a result of the COVID-19 pandemic, medical examinations (RTPCR, X-ray, CT-Scan, etc.) may be required to make a medical decision. COVID-19's SARS-CoV-2 virus infects and spreads in the lungs, which can be easily recognized by chest X-rays or CT scans. However, along with COVID-19 instances, cases of another respiratory ailment known as Pneumonia began to rise. As a result, clinicians are having difficulty distinguishing between COVID-19 and Pneumonia. So, more tests were required to identify the condition. After a few days, the COVID-19 SARS-CoV-2 virus multiplied in the lungs, causing pneumonia and COVID-19 named Novel Corona virus infected Pneumonia. We employ Machine Learning and Deep Learning models to predict diseases such as COVID-19 Positive, COVID-19 Negative, and Viral Pneumonia in this research. A dataset of data is used in a Machine Learning model. A dataset of 120 images was used in the Machine Learning model. By extracting eight statistical elements from an image texture, we calculated accuracy. Adaboost, Decision Tree & Naive Bayes have overall accuracy of 88.46%, 86.4% and 80%, respectively. When we compared the algorithms, Adaboost algorithm performs the best, with overall accuracy of 88.46%, sensitivity of 84.62%, specificity of 92.31%, F1-score of 88% and Kappa of 0.8277. VGG16 Architecture is used in CNN model for 838 images in Deep Learning model. The model's total accuracy is 99.17 %.