Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

TL;DR

This survey addresses the use of deep learning to detect, segment, and forecast COVID-19 via chest X-ray, CT, and ultrasound data, with a focus on X-ray and CT imaging due to accessibility. It catalogues public datasets and systematically categorizes DL techniques into classification, segmentation, forecasting, and advanced AI methods, including GAN-based data augmentation and attention/transformer mechanisms. Key contributions include a comprehensive mapping of DL architectures to COVID-19 imaging tasks, discussion of public datasets, and a synthesis of challenges and future directions for clinical adoption. The findings highlight the predominance of detection research, the crucial role of data availability, and the potential of multi-modal and transformer-based approaches to improve robustness and generalization in real-world settings.

Abstract

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed.

Figures (11)

• Figure 1: The latest detailed statistics of COVID-19 infected people worldwide a7.
• Figure 2: Number of papers published on COVID-19 using DL techniques.
• Figure 3: Illustration of various DL methods used for COVID-19 detection.
• Figure 4: Block diagram for COVID-19 detection using DL technique.
• Figure 5: Overall diagram of pre-trained methods.