Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis

Abstract

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

Keywords: Automated detection; COVID-19; Deep learning; Medical imaging; Radiography; SARS-CoV-2.

Fig. 1

X-ray images with different infection types: a Patchy GGOs present at both lungs; b Nuanced parenchymal thickenings; and c GGOs with some interstitial prominence. Images obtained from [26]

Fig. 2

CT scan showing different infection types: a Subpleural GGOs with consolidations in all lobes; b GGOs with probable partially resolved consolidations; and c Scattered GGOs with band consolidations. Images obtained from [26]

Fig. 3

X-ray images of some faulty images. a Low Contrast with wire around Image. b Textual data on top left corner and probes on chest. c Wires over the chest. Images obtained from [26]

Fig. 4

Distribution of samples among 3 different classes in a original, b upsampled, and c downsampled training dataset

Fig. 5

a–h Confusion matrix of two top performing models generated using four different settings: general cost function, weighted cost function, upsampling training-set, and downsampling training-set. In the figure, GC, WC, QSVM, and ESD denote general cost function, weighted cost function, Quadratic SVM and Ensemble Subspace Discriminant respectively

Fig. 6

Some miss-classified COVID samples. a COVID-19 miss-classified as normal; b–e COVID-19 miss-classified as pneumonia

Fig. 7

2D t-SNE [141] visualization of the extracted features obtained by a DarkNet53 and b DenseNet201 from our X-ray image dataset