id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-346560-jir00627	ELGhamrawy, S. M.	Diagnosis and Prediction Model for COVID19 Patients Response to Treatment based on Convolutional Neural Networks and Whale Optimization Algorithm Using CT Images	2020-04-21		.txt	text/plain	6334	406	57	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. For this reason, in the diagnosing and classification phase of AIMDP, further evaluation from lab tests (RT-PCR and CBC) are used to exclude other causes and accurately diagnosis COVID-19, and doesn't only depend on CT images for diagnosing Finally, AIMDP has a prediction phase that gives a probability of patient ability to respond to the COVID-19 treatment based on different inputs given for the patient like his age, infection stage, respiratory failure, multi-organ failure and the treatment regimens. In the proposed model, different AI techniques are used based on their functionality on six main phases, as shown in figure 2 , namely, the pre-processing, segmentation, feature selection, classification, Prediction and diagnosis recommendation phase.	./cache/cord-346560-jir00627.txt	./txt/cord-346560-jir00627.txt