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.

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

“…Although many recent works are addressed to traditional x-Ray images ( Chandra et al, 2021 , Ismael and Şengür, 2020 , Ismael and Şengür, 2021 ) and use transfer learning ( Vidal et al, 2021 ), lots of novel methods working on CT scans have been proposed. Two recent and comprehensive reviews embrace several methodologies currently used in medical screening ( Ozsahin et al, 2020 , Rahman et al, 2021 ). Obviously, automatic CT scans classification is applied for diverse diseases, as the lung nodule malignancy suspiciousness classification ( Shen et al, 2017 ), but during the last year the main contributions focused on the COVID-19 disease.…”

“…The last column indicates whether the learning is of supervised (S) or unsupervised (U) type. See also [39] and Rahman et al (2021) for a review on the diagnosis of COVID-19 from radiography images. Family Work Approach Type X-ray [28] Multiresolution S [29] Deep learning S [14] Majority voting S [51] U-Net + Transfer Learning S [19] Shallow ML S CT segmentation [55] NormNet U [41] U-Net + SegNet S [18] InfNet S CT binary [43] CNN (CTnet10) S classification [50] VGG-16 S [17] WDNN S [38] Fusion + Majority voting S …”

Role of Artificial Intelligence in COVID-19 Detection

“…This acts as a regularizer and reduces overfitting while training the model. Data augmentation techniques such as rotation, cropping, flipping, and translation [ 49 ], Gaussian blur, and contrast adjustment have been used [ 50 ]. For the class imbalance, SMOTE [ 51 ] has been employed by several authors.…”

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

“…Motivated by this expectation, in the last year, the DL has been successfully used for CXRs [15,20,66,67], CT scans [56,[68][69][70], or both [18,71]. Being, indeed, challenging to summarize all the available literature in a single paper, there are some useful reviews regarding the application of DL techniques to COVID-19 detection on CXRs [72], CT scans [73,74], and both [65,75]. A systematic review on the detection of COVID-19 using chest radiographs and CT scans, highlighting strongness and weakness of several different approaches, can be found in Reference [76].…”

“…A synoptic overview of the related work is provided in Table 1, that summarizes the main approaches pursued by the referred papers. CT [74] CT [75] CXR + CT [65] CXR + CT [76] CXR + CT [77] CXR + CT [78] CXR + CT Segmentation [79] CXR [80] CT [81] CT [82] CT [56] CT [83] CXR [84] CXR CT [85] CT [86] CT [87] CXR [20] CXR [88] CXR Unsupervised [89] Histopathological [90] CT [91] CT [92] CXR…”