Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study.

TL;DR: The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models that are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals.

TL;DR: In this article, the authors proposed a new framework based on deep learning algorithms for recognizing the COVID-19 cases, mostly in public places, including background subtraction for extracting the foreground of thermal images from thermal videos generated by Thermal Cameras through the Thermal Imaging process and the Convolutional Neural Network for detecting people infected with the virus.

TL;DR: In this article , a hybrid deep transfer learning technique has been proposed to detect COVID-19 from chest X-ray images, which achieved better performance in comparison to the existing contemporary transfer learning and deep learning techniques.

TL;DR: The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives and offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models.

TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.