Title

Active Learning Strategy for COVID-19 Annotated Dataset

Author First name, Last name, Institution

Amril Nazir, Zayed University
Ricky Maulana Fajri

Document Type

Article

Source of Publication

IEEE Access

Publication Date

1-1-2021

Abstract

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.

ISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

PP

Issue

99

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

COVID-19, Deep learning, Annotations, X-ray imaging, Costs, Mathematical models, Labeling

Indexed in Scopus

no

Open Access

no

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