An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Abstract

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant’s technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Role of Artificial Intelligence in COVID-19 Detection

“…Investigators have developed many models to detect COVID-19 during the past two years and have shown that there is a role for AI in detecting COVID-19 [ 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 277 , 278 , 279 , 280 , 281 ]. The 184 technical papers reviewed in this study provide up-to-date knowledge on the usage of AI techniques in detecting COVID-19.…”

“…These automated tools using cost-effective imaging modalities assist to address COVID-19 by preventing false negative reports, and can be used in the case of scarcity or non-availability of RT-PCR test kits in rural areas. Researchers have reported exhaustive studies using imaging modalities for the detection of COVID-19 [ 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Although these review papers have shown the significance of deep learning and machine leaning algorithms for automated detection, this paper explores the following key points in association with detection: The state-of-the-art AI techniques (deep neural network (DNN) and hand-crafted feature learning (HCFL) based models) used to detect COVID-19.…”

Indirect supervision applied to COVID-19 and pneumonia classification

“…Several overviews into the application of CNN techniques to aid in COVID-19 diagnosis have been conducted and we refer the reader to Refs. [ [35] , [36] , [37] ] for more details.…”

DCML: Deep contrastive mutual learning for COVID-19 recognition

“…Contrarily, health workers need to receive proper training to collect PCR samples, while the processing and generation of CT or X-ray images are relatively easy. Therefore, the computer-aided COVID-19 recognition method has significant practical value, which helps reduce the burden of RT-PCR and speed up diagnosis efficiency [3] , [4] .…”