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

After calculating the fitness function for each feature, the fitness values are compared with the previous one. If the new values are greater than old ones, then the search agents replace the old values with the new values of solutions, otherwise the old solutions are retained, as shown in figure 3 . The encircle prey module arrange the fitness values obtained then it searches for the optimum solution in the neighbour of the best-known solution using the Distance Ratio Test (DRT) technique to detect the exact location of the solution(prey) in terms of (x, y) dimensions, during the searching process of preys [18] . In the Bubble-Net Attacking Method Applier module, a Bubble-Net Attacking Method Applier (BNAM) technique is used, first proposed by [18] , to switch between the attacking methods of the whale depends the location of the prey. Based on the solution's location detected and compared with other locations, BNAM select the suitable mechanism for this location, it switches between the shrinking encircling and spiral mechanism. In the termination module, the maximum number of iterations ( ) is predefined from the beginning, . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.