Algorithm 1. Majority voting algorithm using ensemble of benchmark classifiers.
Input/Initialize
Validation dataset $D=\{x_1,x_2,\cdots \cdots x_n\}$. // $n$=number of images in the dataset
Classification model $M=\{m_1,m_2,\cdots \cdots m_k\}$ //$k$ = number of classification models
Healthy = 0; Unhealthy = 0;
Output
Prediction results of each image as healthy or infected.
Algorithm
1: $\mathit{f}\mathit{o}\mathit{r}{x}_i=1ton$ //where, $n$=number of images in the dataset 2:  Healthy = 0; Unhealthy = 0; 3:   $\mathit{f}\mathit{o}\mathit{r}{m}_j=1tok$ // where, $k$=number of classification models 4:     $x_{i.class}^m\leftarrow \mathit{p}\mathit{r}\mathit{e}\mathit{d}\mathit{i}\mathit{c}\mathit{t}({\mathit{x}}_{\mathit{i}},{\mathit{m}}_{\mathit{j}})$ // extract the class label using model $m_j$ for input image $x_i$ 5:     $\mathit{i}\mathit{f}{x}_{i.class}^m=\text{'}Healthy\text{'}$ // jth model classify the data as ‘Healthy’ 6:      Healthy = Healthy + 1; // increment the counter by one 7:     else $\mathit{i}\mathit{f}{x}_{i.class}^m=\text{'}Unhealthy\text{'}$ // jth model classifies the data as abnormal 8:      Unhealthy = Unhealthy + 1; // Increment the probability by one 9:     end 10:   end 11:   $\mathit{i}\mathit{f}Healthy>Unhealthy$ 12:     $x_{i.class}{=}^{\text{'}}Health{y}^{\text{'}};$ 13:   else 14:     $x_{i.class}{=}^{\text{'}}Unhealth{y}^{\text{'}};$ 15:   end 16: end