## 4 Results and Discussions
### Experimental Findings
#### 4.2.2 Multi-Class Classification  
Classification of red meat images into fresh, half-fresh, and spoiled is performed by ConvNet-24. The efficacy of the model is investigated on both original and augmented MEAT3C database. An overall classification accuracy of 94.2% is reported when the model is evaluated on the original database. The performance may be improved for the larger dataset. Hence, the training images are enlarged using rotation augmentation technique, whereas the testing images are unaltered. With this augmented dataset, the model is trained and tested in various configurations. Table 4 depicts the metrics obtained by the ConvNet-24 model. The macro-average sensitivity \(\mathit{Sen}_{M}\)[29] is computed by summing the sensitivity of individual classes and is given by Equation (15). \[\mathit{Sen}_{M}=\frac{\sum_{i=1}^{N}\frac{tp_{i}}{tp_{i}+tn_{i}}}{N} \tag{15}\]  
The percentage of fresh meat images correctly identified is denoted by \(\mathit{Sen}_{F}\). \(\mathit{Sen}_{HF}\) and \(\mathit{Sen}_{SP}\) indicate the percentage of half-fresh and spoiled meat images identified correctly by the model. The mathematical equations for computing the sensitivity is defined in Equations (16)-(18) as follows:  
\[\mathit{Sen}_{F}=\frac{tp_{\mathit{Fresh}}}{tp_{\mathit{Fresh}}+tn_{\mathit{ Fresh}}}, \tag{16}\]  
where \(tn_{\mathit{Fresh}}\) denotes false negatives of the fresh class. It can be computed by adding the number of fresh samples that the model incorrectly identified as half-fresh and spoiled classes:  
\[\mathit{Sen}_{HF}=\frac{tp_{\mathit{Halfresh}}}{tp_{\mathit{Halfresh}}+tn_{ \mathit{Halfresh}}}, \tag{17}\]  
where \(tn_{\mathit{Halfresh}}\) is false negatives of the half-fresh class. It can be computed by adding the number of half-fresh samples that the model misclassified as fresh and spoiled classes:  
\[\mathit{Sen}_{SP}=\frac{tp_{\mathit{Spoiled}}}{tp_{\mathit{Spoiled}}+tn_{ \mathit{Spoiled}}}, \tag{18}\]  
where \(tn_{\mathit{Spoiled}}\) is false negatives of the spoiled classes. It can be computed by adding the number of spoiled samples that the model misclassified as fresh and half-fresh classes. The macro-average precision \(\mathit{Pre}_{M}\) is computed by summing the precision of individual classes and is given in Equation (19) as  
\[\mathit{Pre}_{M}=\frac{\sum_{i=1}^{N}\frac{tp_{i}}{tp_{i}+fp_{i}}}{N}, \tag{19}\]  
The precision of individual classes is represented as \(\mathit{Pre}_{F}\), \(\mathit{Pre}_{HF}\), and \(\mathit{Pre}_{SP}\) which are computed using the equations defined in Equations (20)-(22):  
\[\mathit{Pre}_{F}=\frac{tp_{\mathit{Fresh}}}{tp_{\mathit{Fresh}}+fp_{\mathit{ Fresh}}}, \tag{20}\] \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Performance** & & & & & & & **Training** & **Testing** \\
**Metrics/** & _Acc_ & _Sen_ & _Spe_ & _Pre_ & _F1-Score_ & **Time** & **Time** \\
**Configurations** & & & & & & & **in s** & **in s** \\ \hline Conf1 & 99.6 & 100 & 99.1 & 99.1 & 99.5 & 1949.26 & 37.62 \\ Conf2 & 99.4 & 99.8 & 99 & 99 & 99.4 & 1956.34 & 38.12 \\ Conf3 & 99.3 & 99.7 & 98.9 & 99 & 99.3 & 1945.81 & 37.2 \\ Ensem & 99.4 & 99.8 & 99 & 99 & 99.4 & 5860.91 & 115.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics obtained using ConvNet-18. where \(fp_{Fresh}\) is false positives of the fresh class. It can be computed by adding the number of half-fresh and spoiled samples that the model misclassified as fresh class. \[Pre_{HF}=\frac{tp_{HalfFresh}}{tp_{HalfFresh}+fp_{HalfFresh}}, \tag{21}\]  
where, \(fp_{HalfFresh}\) is false positives of the half-fresh class. It can be computed by adding the number of fresh and spoiled samples that the model misclassified as half fresh class. \[Pre_{SP}=\frac{tp_{Spoiled}}{tp_{Spoiled}+fp_{Spoiled}}, \tag{22}\]  
where, \(fp_{Spoiled}\) is false positives of the spoiled classes. It can be computed by adding the number of fresh and half-fresh samples that the model misclassified as spoiled class.