Stacking Deep Learning for Early COVID-19 Vision Diagnosis

Hammam, Ahmed A.; Elmousalami, Haytham H.; Hassanien, Aboul Ella

early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists and health care workers to take reliable corrective actions for classify patients and detecting the COVID 19 confirmed cases. Prediction and classification accuracy are critical for COVID-19 diagnosis application. Current practices for COVID-19 images classification are mostly built upon convolutional neural network (CNNs) where CNN is a single algorithm. On the other hand, ensemble machine learning models produce higher accuracy than a single machine leaning. Therefore, this study conducts stacking deep learning methodology to produce the highest results of COVID-19 classification. The stacked ensemble deep learning model accuracy has produced 98.6% test accuracy. Accordingly, the stacked ensemble deep learning model produced superior performance than any single model. Accordingly, ensemble machine learning evolves as a future trend due to its high scalability, stability, and prediction accuracy.