A review of deep learning-based detection methods for COVID-19

Abstract

COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.

Keywords: COVID-19 detection; Coronavirus pandemic; DL-Based COVID-19 detection; Lung image classification; Medical image processing.

Graphical abstract

Fig. 1

Overall workflow summary of all the methods. The first step is the acquisition of the data, and the imaging format can be chest X-ray (CXR) or CT scan. The second step is preprocessing, such as image resizing and data augmentation. Then, the preprocessed data are trained using one of the three methods. The trained model is used for classification and evaluation.

Fig. 2

Stepwise diagrammatic representation of transfer learning by Chowdhury et al. [46]. The first step is the acquisition of the patients' data from an X-ray imaging machine. Both two-class classification and three-class classification are performed. Second, in the image resizing (preprocessing) step, the input layer of the deep learning method is fit. Data augmentation is performed in one of the experiments. Then, transfer learning is performed on various deep learning architectures. Finally, the trained model is saved, and classification is performed.

Fig. 3

Methods and approaches. The surveyed literature works are grouped into three categories, namely, transfer learning and fine-tuning, novel architectures, and other approaches. Three branches are included in the figure, and the methods under each category are listed.

Fig. 4

(a). Example images from two classes, namely, COVID+ and COVID-, from the [90] dataset. (b). Example images from three classes, namely, normal, viral pneumonia and bacterial pneumonia, from the [91] dataset.

Fig. 5

Graphical representation of the accuracy results for novel architectures and other approaches.