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. 2022 Jul 14;10(7):1313. doi: 10.3390/healthcare10071313
Algorithm 1. Pseudo code of the proposed scheme.
Proposed Algorithm
01 ImP = Load (PeN-CoVx)  // Read PeN-CoVx image dataset
02 ImNorm = (ImP – min(ImP)) / (max(ImP) – min(ImP))  // Normalize images within range [0, 1]
03 ImSWC = CSW(ImNorm)  // Data augmentation: 3-levels decomposition by stationary wavelet transformation
04 ImAug = DataAugmentation(ImSWC)  // Performing random rotation, translation, and shear operation
05 for i = 1 to size-of ImAug do // Loop to extract relevant features for each image using transfer-learning model
06    fg1 = AlexNet(ImAug)  // Extract 1000 features via AlexNet
07    fg2 = ResNet101(ImAug)  // Extract 1000 features via ResNet101
08    fg3 = SqueezeNet(ImAug)  // Extract 1000 features via SqueezeNet
09    for j = 1 to 3 do // Loop to merge extracted feature
10       X(i, 1000 × j + 1: 1000 × (j + 1)) = fgj  // merge extracted features
11    end for loop // end loop to merge extracted feature
12 end for loop
13 fs1 = iNCA(X, Y)  // Determine optimal features via iterative Neighborhood Component Analysis
14 fs2 = iChi2(X, Y)  // Determine optimal features via iterative Chi-square
15 fs3 = iMRMR(X, Y)  // Determine optimal features via iterative Maximum Relevance Minimum Redundancy
16 PLk = CNN(fsk, Y, 3)  // Predict clinical-state using optimal features by Convolutional Neural Network
17 PLk = LDA(fsk, Y, 3)  // Predict clinical-state using optimal features by Linear Discriminant Analysis
18 PLk = SVM(fsk, Y, 3)  // Predict clinical-state using optimal features by Support Vector Machine