Algorithm 1: The Overall Algorithm of the Proposed System

Training Protocol for Feature Extraction using deep learning

Obtain the chest X-ray images of COVID-19 as training data. Crop those chest X-ray images at random to 224 × 224 and rotate them at random by 30°. Input the transformed chest X-ray images obtained in step 2 into the CNN classifier for fine-tuning and begin the training of the model. When training is completed, extract the desired output layer features and save the model. Testing Protocol using Similarity Measure 5.Obtain the chest X-ray images from the database of previous COVID-19 cases. 6.Resize the chest X-ray images from the COVID-19 database to 225 × 225 and perform a centre crop of 224 × 224. 7.Extract and store the feature vectors of chest X-ray images from the database using the pre-trained CNN model. 8.Calculate the similarity of the query image feature vector with all the stored database feature vectors. 9.Find the top-k similar feature vectors in the database, where k is a positive integer. 10.Retrieve the chest X-ray images with their records of meta-data from the database, corresponding to the top-k similar feature vectors obtained in step 6. 11.Recommend the doctors, medicines, and resources present in the retrieved meta-data records to the new patient as the output.