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

Whale Optimization Algorithm WOA is one of the latest nature-inspired metaheuristic algorithm introduced by Mirjalili [15] , WOA emulates the social behaviour of humpback whale when hunting the prey. A detailed descriptions of the whales' behaviours can be found in [16] . The feature selection phase in AIMDP use the WOA algorithm to select the most optimal relevant features, from the detailed patient information, to distinguish COVID 19 disease from different vial phonemes. The FS phase consists of five modules: Population initialization, Fitness Function, Encircle Prey, Bubble-Net Attacking Method Applier and Termination Module. In the Population initialization module, a number of search agents (whales) is randomly initialized, each whale represents a subset of features, to target the most relevant features (Preys). The appropriate representation of the feature/ patient list will help searching for the optimum solution and optimize the accuracy of diagnosing COVID19 disease. In the fitness function module, these agents(features) are converted into possible solutions for a given fitness function and initial generation of WOA parameters are set. Then for each solution, a fitness function will be evaluated for this solution. WOA uses fitness function that selects the minimal set of features for a patient achieving the best accuracy in diagnosing the virus. The most common CT finding in COVID-19 is the Ground Glass Opacities (ḠḠỌ) They represent tiny air sacs (alveoli) filling with fluid and turning a shade of grey in