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 addition, Yang et al. [11] investigated the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19. Their analysis suggests that chest CT should be considered for the COVID-19 screening, comprehensive evaluation, and following-up, especially in epidemic areas with high pre-test probability for disease. Jiang et al [12] proposed established an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The authors used multiple CNN models to classify CT image datasets and calculate the infection probability of COVID-19. The findings might greatly assist in the early screening of patients with COVID-19 by deep learning technologies. The authors proposed a location-attention mechanism and uses it in the classical ResNet for feature extraction. The authors in [13] constructed a system based on deep learning for identification of viral pneumonia on CT.