Algorithm 1: Majority Voting.

Input: n Models or classifiers, training samples with ground truth and test samples.

Output: Predicted class labels, label probability score, and performance evaluation.

Step 1. Train all n models on the same training set.

Step 2. Take a sample from test set and test it through trained model and predict the label in terms of probability score.

Step 3. Measure a model’s vote for each label by the following rule.             IF Probability (label ‘LGG’) > 0.5 THEN                   Vote (LGG) = 1 and Vote (HGG) = 0.            Otherwise,                   Vote (LGG) = 0 and Vote (HGG) = 1

Step 5. Repeat step 2 and 3 for all the trained models.

Step 6. Calculate the total number of votes for each label predicted by all trained models by the following rule.            IF (Total Vote ‘LGG’) > (Total Vote ‘HGG’) THEN                  Label ‘LGG’ will be predicted.           Otherwise,                  Label ‘HGG’ will be predicted.

Step 7. Repeat Step 2 to Step 6 for all the test samples.

Step 8. Compare predicted labels of each sample to the actual ground truth and create confusion matrix.