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

In this phase, the performance of each classifier's prediction is evaluated based on further evaluation from lab tests, like RT-PCR and Complete Blood Count CBC, to exclude other causes and to accurately diagnosis COVID-19. Diagnosis phase uss CT chest scans to diagnosis COVID-19 based on the relevant signs extracted. As we mentioned before that the ground glass opacities is usuall the first sign of COVID19 and it can apear isolated or combined with consolidations and cravy paving pattern. These finding usually appear in multiple lobes in both lungs and outer periphery of the lungs. In mild and recovery cases of COVID19, the sings can be isolated to be in just one lobe. The motivation of finding these three signs were be more likely to be recognized as COVID-19. Otherwise, these finding can also be seen in other causes of Viral pneumonias like influenza and adenovirus. That means, in this case the diagnosing cant depend on chest CT scan images only, as it can't be reliable. The diagnosing phase uses further evaluation from lab tests to exclude other causes and accurately diagnosis COVID-19. The recommendations are then sent to the clinical, that must deal with the challenge of highly increasing numbers of patient and analysing high-dimensional patients' data, to give a final decision for a specific patient.