id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-231762-ymz7z76i	Misra, Sampa	Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19	2020-05-12		.txt	text/plain	3695	189	54	In this manuscript, we have designed a multi-channel ensemble TL method based on ResNet-18 by combining three different models which are fine-tuned in 3 datasets in such a way that the model can extract more relevant features for each class and hence can identify COVID-19 features more accurately from the X-ray images. These 3 models were pre-trained in parallel to learn respective features to classify normal, pneumonia, and COVID-19 images. 8. Train (fine-tune) again the combined models using the Dataset_D, which can classify the normal, pneumonia, and COVID-19 images. In this manuscript, we present a COVID-multichannel transfer learning method for the classification of patients as normal, COVID-19, and pneumonia based on chest X-ray images. Besides, there are many artifacts in the chest X-ray images that may negatively affect the performance of classification tasks for feature-based deep learning models. In conclusion, we propose an ensemble learning strategy to improve the classification performance in deep learning-based COVID-19 screening for chest X-ray images.	./cache/cord-231762-ymz7z76i.txt	./txt/cord-231762-ymz7z76i.txt