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

TL;DR

This paper reviews how deep learning techniques are applied to diagnose, segment, and forecast COVID-19 using X-ray and CT images, highlighting current challenges and future research directions.

Contribution

It provides a comprehensive survey of deep learning applications for COVID-19 detection, segmentation, and forecasting, summarizing recent studies and identifying research gaps.

Findings

Deep learning models improve COVID-19 detection accuracy from medical images.

DL techniques enable automated lung segmentation and disease classification.

Challenges include data scarcity and model generalization issues.

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.