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