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

Due to the rapid expansion of AI technology that has been commonly applied in the medical field, different studies were conducted on the diagnosis and classification of different diseases like viral pneumonias and organs' tumours. Nowadays, due to the COVID-19 spreading disaster, many researches have focused on diagnosing and detecting COVID-19 as follows: Authors in [9] proposed a 3D deep convolutional neural Network to Detect COVID-19 from CT volume, namely DeCoVNet. But, the algorithm worked in a black-box manner when diagnosing COVID-19, since the algorithm was based on deep learning and its explain ability was still at an early stage. COVNET [10] developed a framework to detect COVID-19 using chest CT and evaluate its performances. The authors proposed a three-dimensional deep learning framework to detect COVID-19 using chest CT. Community acquired pneumonia (CAP) and other nonpneumonia exams were included to test the robustness of the model.