Source of Publication
The efficient diagnosis of COVID-19 plays a key role in preventing its spread. Recently, many artificial intelligence techniques, such as the deep neural network approach, have been implemented to help efficient diagnosis of COVID-19. However, the accurate performance of deep learning depends on the tuning of many hyperparameters and a large amount of labeled data. This COVID-19 data bottleneck also leads to insufficient human resources for data labeling, which presents a challenging obstacle. In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation. The framework specifically designed to suit the imbalanced data phenomenon that is characteristic of COVID-19 data. Extensive experiments over four public real-world COVID-19 datasets from several countries such as Brazil, China, Israel and Mexico show that our active learning framework significantly outmatches other state-of-the-art models. Our proposed framework achieves an average G-Mean of 10% improvement for the four datasets. Finally, the results of significance testing verify the effectiveness of DS3 and its superiority over baseline active learning algorithms.
Institute of Electrical and Electronics Engineers (IEEE)
Computer Sciences | Medicine and Health Sciences
COVID-19, Deep learning, Annotations, X-ray imaging, Costs, Mathematical models, Labeling
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Nazir, Amril and Fajri, Ricky Maulana, "Active Learning Strategy for COVID-19 Annotated Dataset" (2021). All Works. 4681.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series