Algorithm 1. Classification of X-ray chest diseases by using m-Xception and XgBoost classifier
Step 1:	Pre-process image = X and Pre-processing step is applied by using: (a) Resize Chest X-Ray image (X) to (299, 299) [Enhanced Chest X-Ray image by preprocessing steps] (a) Remove Noise using Gaussian smoothing operator, and (b) Enhance local contrast logarithmic operator
Step 2:	Class Balance = Augmentation (preprocessed)
Step 3:	[Extract Deep Feature]: (a) Feature Extraction used: Optimize the m-Inception model by first entry flow for the feature extraction
Step 4:	Deep-features = Deep features were extracted by the m-Xception model
Step 5:	Prediction = XgBoost classifier is used to classify the images into four classes: lung opacity, COVID-19, pneumonia, and normal
Step 6:	[End]