COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm

Abstract

Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict Covid spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT- PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of Deep Transfer-learning models, this research investigates the potential of Artificial Intelligence -based early diagnosis of COVID-19 via X-ray chest radiographs. With 10,192 normal and 3616 Covid X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. The x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the Deep Transfer-learning models. The Deep Transfer-learning architectures are trained using the extracted features from the Feature Extraction stage. During training, the classification of X-ray Chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection. The feature label set is further tested with the SVM, KNN, NN, Naive Bayes and Logistic Regression classifiers to audit the quality metrics of the proposed model. The quality metrics include accuracy, precision, F1 score, recall and AUC. The Inception-V3 dominates the six Deep Transfer-learning models, according to the assessment results, with a training accuracy of 84.79% and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and F1 score of 0.864. Wide NN fared better than the other NN classifiers with an AUC score of 0.98, precision of 0.975, recall of 0.907, accuracy of 95.5%, and F1 score of 0.939. According to the findings, SVM classifiers topped other classifiers in terms of performance indicators like accuracy, precision, recall, F1-score, and AUC. The SVM classifiers reported better mean optimal scores compared to other classifiers. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis.

Keywords: Boundary tracing; Covid diagnosis; Deep transfer-learning; Medical imaging; Neural network models and classifiers.

Fig. 1

Proposed methodology.

Fig. 2

Examples of X-rays images used in the Proposed Methodology.

Fig. 3

Layer description of deep transfer-learning models.

Fig. 4

AlexNet architecture.

Fig. 5

AlexNet model's accuracy and loss-function plot.

Fig. 6

SqueezeNet architecture.

Fig. 7

SqueezeNet model's accuracy and loss-function plot.

Fig. 8

Inception-V3 architecture.

Fig. 9

Inception-V3 model's accuracy and loss-function plot.

Fig. 10

ResNet-50 architecture.

Fig. 11

ResNet-50 model's accuracy and loss-function plot.

Fig. 12

VGG-16 architecture.

Fig. 13

VGG-16 model's accuracy and loss-function plot.

Fig. 14

GoogleNet architecture.

Fig. 15

GoogleNet model's accuracy and loss-function plot.

Fig. 16

AUC-ROC plot of the SVM classifiers.

Fig. 17

AUC-ROC plot of the KNN classifiers.

Fig. 18

AUC-ROC plot of the Naive Bayes and Logistic regression classifiers.

Fig. 19

AUC-ROC plot of the NN classifiers.

Fig. 20

Classifiers' performance metrics comparison.