A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction
Date
2022-01-17Metadata
Show full item recordAbstract
As the COVID19 pandemic evolves and coronavirus mutates to different variants, a high workload falls on the shoulders of doctors and radiologists. Identifying COVID19 through X-ray and Computed Tomography (CT) scanning in a short amount of time is vital because it helps doctors start the COVID19 treatment in the early stages. Deep Learning algorithms showed tremendous results in automating COVID19 detection using X-ray and CT scans. As there are not many survey papers on COVID19 detection using deep learning techniques, the goal of this paper is (1) to give a thorough discussion of COVID19 prediction considering Computer Vision problems like COVID19/pneumonia classification, detection, and segmentation, (2) to address new advances in deep learning like Transformers, GANs, and LSTMs, and (3) to cover technical issues like data security and data scarcity of X-ray and CT scans in COVID19.
Collections
- Kok-Seng Wong, PhD [19]
Related items
Showing items related by title, author, creator and subject.
-
Employee learning in tourism experiences during Covid-19: A communities of practice perspective
(2023-06-26)The global Covid-19 pandemic means that most tourism organizations face a heightened need to offer a range of innovative tourism experiences in order to survive. The successful delivery of these experiences largely depends ... -
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource-Constrained Devices Using Divide and Collaborative Training
(2024-01)In Federated Learning (FL), the size of local models matters. On the one hand, it is logical to use large-capacity neural networks in pursuit of high performance. On the other hand, deep convolutional neural networks (CNNs) ... -
Few-Shot Learning based on Residual Neural Networks for X-ray Image Classification
(2022)Currently, deep learning is widely used in the field of medicine, which includes radiology. This paper addresses the classification of X-ray images, particularly focusing on the challenge of insufficient images for specific ...
