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Computers, Materials & Continua
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DOI: 10.32604/cmc.2023.040560

Editorial

Deep Learning for COVID-19 Diagnosis via Chest Images

Shuihua Wang1,2 and Yudong Zhang2,*

1School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
2Department of Information Systems, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
*Corresponding Author: Yudong Zhang. Email: yudongzhang@ieee.org
Received: 12 March 2023; Accepted: 12 April 2023; Published: 09 June 2023

COVID-19 is a vastly infectious disease caused by the new coronavirus, officially recognized as severe acute respiratory syndrome coronavirus 2 [1]. This virus has multiplied fast worldwide, causing a global pandemic [2]. It has caused 6.87 million death tolls until 20/March/2023.

The easiest way to stop COVID-19 is to diagnose and quarantine infected patients as rapidly as possible [3]. Both reverse transcription polymerase chain reaction (RT-PCR) [4] and real-time RT-PCR [5] are commonly used to detect the virus nucleic acid from nasopharyngeal swabs. However, they suffer from obvious shortcomings, such as environmental contamination [6], slow reporting (it costs from hours to weeks to receive the outcomes), and high false-negative results [7].

Chest imaging [8] is an effective method for rapid analysis of COVID-19. It is one type of medical imaging technique to examine the body’s chest or thorax area, including the heart, lungs, and other structures. Four common chest imaging scan modalities are available, chest X-ray radiograph (CXR) [9], chest computed tomography (CCT) [10], chest magnetic resonance imaging [11], and chest ultrasound [12]. In clinical practice, CXR and CCT are particularly suitable for diagnosing COVID-19.

Nevertheless, manual diagnosis of COVID-19 from CXR or CCT is a tedious task that requires significant time and effort. The lesions of COVID-19 are displayed with prominent signs of ground-glass opacities (GGOs) [13] in CCT. Moreover, it is challenging for junior radiologists with limited experience to detect GGO regions [14] from chest images. Besides, the manual diagnosis is possibly affected by loads of aspects (sentiment, tiredness, exhaustion, etc.).

However, machine learning (ML) algorithms [15] constantly and precisely follow the pre-designed instructions more speedily and consistently than radiologists do. Additionally, the lesions of early-phase COVID-19 lungs are minor and marginal, like the adjacent healthy tissues. Those small lesions are effortlessly perceived by ML methods [16] but possibly neglected by human experts.

Deep learning (DL) [17] is nowadays the most successful type of ML algorithm. Compared to standard ML algorithms, DL has the following five advantages: (i) DL algorithms normally have higher accuracies than traditional ML algorithms. (ii) DL algorithms have better feature extraction abilities, eliminating the need for manual feature engineering. (iii) DL models can be easily scaled to large-size datasets [18], which is particularly suitable for big data applications. (iv) DL algorithms are more flexible than traditional ML algorithms [19]. (v) DL algorithms do not need as much human help as traditional ML algorithms, reducing human intervention [20].

Many successful DL algorithms have been proposed in previous years. One successful algorithm is the deep rank-based average pooling network (DRAPNet) [21], published in this journal. This paper was indexed as ESI highly cited paper recently. In the paper, the authors first introduce enhanced multiple-way data augmentation [22]. Second, the authors present the n-conv rank-based average pooling module [21]. Third, the authors propose their DRAPNet based on NRAPM. Finally, heatmaps are produced based on Grad-CAM for explainable analysis. The authors tested their DRAPNet algorithm on a four-class dataset. DRAPNet has attained a 95.49% micro-averaged F1 score [21].

There are many other successful DL-based COVID-19 diagnosis algorithms, such as AVNC [23], deep stacked sparse autoencoder analytical (DSSAE) [24], IFFA-DTLMS [25], deep stacked ensemble learning model [26], ANC [27], etc.

Because of the limited sizes of the open-access datasets with ground truth labels, weakly supervised learning (WSL) [28], trained by weak labels or partial annotations, is particularly suitable for constructing efficient COVID-19 models [29]. WSL can attain excellent results for limited-labeled datasets, where acquiring accurately labeled samples are expensive or challenging [30].

COVID-19 has been widespread worldwide for three years and five months. We believe ML and DL can continue to help diagnose COVID-19. Firstly, those algorithms can be used to develop more accurate and reliable algorithms for automated diagnosis of COVID-19, which can save radiologists time and effort. Secondly, ML and DL algorithms can predict COVID-19 disease progression and prognosis [31], helping identify high-risk patients who may require more intensive treatment. Thirdly, ML and DL can identify patterns and correlations between imaging findings and clinical outcomes, leading to the discovery of new biomarkers [32] and more effective treatments.

Overall, ML and DL have the potential to significantly improve the accuracy and efficiency of COVID-19 diagnosis and treatment planning based on chest images.

Funding Statement: This paper is partially supported by 12 UK grants: Sino-UK Education Fund (OP202006); MRC (MC_PC_17171); Royal Society (RP202G0230); BHF (AA/18/3/34220); Hope Foundation for Cancer Research (RM60G0680); GCRF (P202PF11); BBSRC (RM32G0178B8); Sino-UK Industrial Fund (RP202G0289); LIAS (P202ED10 & P202RE969); Data Science Enhancement Fund (P202RE237); Fight for Sight (24NN201).

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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