Diagnosis and Prediction Model for COVID19 Patients Response to Treatment based on Convolutional Neural Networks and Whale Optimization Algorithm Using CT Images

Sally M. ELGhamrawy; Abou Ellah Hassanien

Even though chest CT is very sensitive to COVID19, there are some COVID 19 key findings like (Ground Glass Opacities, Solid White Consolidation and Crazy Paving Pattern) that also can be seen in other causes of Viral pneumonias like influenza and adenovirus. That means, the chest CT scan images can be sensitive but not specific for COVID19. For this reason, in the diagnosing and classification phase of AIMDP, further evaluation from lab tests (RT-PCR and CBC) are used to exclude other causes and accurately diagnosis COVID-19, and doesn't only depend on CT images for diagnosing Finally, AIMDP has a prediction phase that gives a probability of patient ability to respond to the COVID-19 treatment based on different inputs given for the patient like his age, infection stage, respiratory failure, multi-organ failure and the treatment regimens. This predicted value is given to the doctors that make a decision to provide the patient with treatment and intensive care services. This will help the doctors to save the resources of endemic countries, that have no enough resources, and only give the treatment to the responding patients.