Algorithm 1: Proposed Modified MobileNetV2 Algorithm for COVID-19 Detection

Input: 52,000 chest X-ray images. (80% train, 20% test data)

Output: Result = COVID-19 positive or healthy

Step 1: batch normalization, preprocessing, augmentation

Step 2: Freeze the base layer and add proposed convolution layer with image size

224, kernel size (3, 3), optimizer = RMSprop, activation: ReLU

Step 3: Feed the first residual convolution layer with kernel size (2, 2), activation =

ReLU, then average pooling, optimizer = RMSprop

Step 4: Feed into the second residual convolution layer with kernel size (1, 1), stride = 2.

Average pooling, dropout, optimizer = none

Step 5: Feed into the third residual convolution layer with kernel size (2, 2), stride = 1.

Max pooling, dropout, optimizer = RMSprop

Step 6: Feed into the fourth and fifth residual convolution layer with kernel size (1, 1),

stride = 2. Max pooling, dropout, optimizer = none

Step 7: Feed into sixth residual convolution layer with kernel size (2, 2), stride = 2, no

pooling, dropout, optimizer = RMSprop

Step 8: Feed into seventh residual convolution layer with kernel size (2, 2), stride = 1,

average pooling, dropout, optimizer = RMSprop

Step 9: Apply proposed layer with image size 224 × 224, kernel size (3, 3), optimizer =

RMSprop, activation: ReLu

Step 10: Finding the accuracy, precision, f1 score, recall