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

segmentation, can process hundreds of CT images in seconds to speed up diagnosis of COVID-19 and contribute in its containment. To accurately detect the signs of COVID-19 in CT images, a Feature Selection phased bases on Whale Optimization Algorithm (FSWOA) is proposed for selecting the most relevant patient's features. The most common signs for COVID 19, detected on CT scans are the Ground Glass Opacities (ḠḠỌ), that represent tiny air sacs (alveoli) filling with fluid and turning a shade of grey in CT scan. In severe infections and more advanced infections, more fluid will be occurring in lobes of the lungs, so the ground glass opacities will progress to Solid White Consolidation(ṨẀḈ) sign. However, the Crazy Paving Pattern(ḈṔṔ) swelling due the swelling in interstitial space along the walls of the lungs that makes the wall looks thicker like the white lines against the hazy ground glass background grey. The ḠḠỌ is usually the first sign of COVID19, and followed later by one or both of the other signs. Moreover, some countries haven't the ability to provide all patients with the treatment and intensive care service, it will be mandatory to give treatment to only responding patients. In this context, the Prediction Module (PM) is proposed for predicting the ability of the patient to respond to treatment based on different factors e.g. age, infection stage, respiratory failure, multi-organ failure and the treatment regimens. PM implement the Whale Optimization algorithm for selecting the most relevant patient's features. The experimental results show promising performance for the proposed diagnosing and prediction modules, using a dataset with hundreds of real data and CT images.The rest of the paper is organized as follows: Section 2 shows the recent COVID-19 diagnosing models proposed, and a background of WOA is introduced. Section 3 demonstrates the proposed Artificial Intelligence-inspired Model for COVID-19, the Diagnosis (DM) and the Prediction Module (PM) are also discussed in details. The performance of AIMDP is evaluated in section 4 showing the effect of implementing FSWOA and using CNNs as a deep learning technique. The results are compared against the most recent diagnosing models'.