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

The copyright holder for this preprint . https://doi.org/10.1101/2020.04. 16.20063990 doi: medRxiv preprint Figure 9 reveals the significant superiority of AIMDP over the other models in terms of precision, accuracy, and sensitivity. In addition, figure 10 shows that AIMDP has the lower execution time needed compared to CorrCT [11] , COVNet [ 10] , DeCon-Net [9] and ReNet+ [12] . This is due to the presence of many processes included in their executions which may need longer time. Moreover, using the deep learning methods in diagnosing is time consuming. While AIMDP only consider the lung regions and remove the noise in the non-lung regions in the pre-processing phase to avoid time consuming in segmenting the whole image using CNNs.