## 4 Results and Discussion
Likewise, the BoW-LBP and GLCM-SVM models have accomplished slightly improved average \(prec_{n}\) values of 74.68% and 75.47% respectively. At the same time, the GoogleNet and VGGNet techniques have resulted in  
Figure 5: Confusion matrices of MDTL-ICDDC technique (**a**) epoch 200, (**b**) 400 epoch, (**c**) epoch 600, (**d**) epoch 800, (**e**) epoch 1000, and (**f**) epoch 1200.  
reasonable average \(prec_{n}\) values of 82.98% and 86.14% respectively. However, the MDTL-ICDDC model has gained maximum average \(prec_{n}\) of 92.90%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **G-Measure** \\ \hline \multicolumn{6}{c}{Epoch-200} \\ \hline Class-0 & 95.60 & 92.92 & 89.20 & 91.02 & 91.04 \\ \hline Class-1 & 94.10 & 88.35 & 88.00 & 88.18 & 88.18 \\ \hline Class-2 & 96.40 & 91.47 & 94.40 & 92.91 & 92.92 \\ \hline Class-3 & 98.30 & 96.05 & 97.20 & 96.62 & 96.62 \\ \hline Average & 96.10 & 92.20 & 92.20 & 92.18 & 92.19 \\ \hline \multicolumn{6}{c}{Epoch-400} \\ \hline Class-0 & 96.00 & 94.49 & 89.20 & 91.77 & 91.81 \\ \hline Class-1 & 94.90 & 90.28 & 89.20 & 89.74 & 89.74 \\ \hline Class-2 & 96.60 & 91.86 & 94.80 & 93.31 & 93.32 \\ \hline Class-3 & 98.30 & 94.98 & 98.40 & 96.66 & 96.68 \\ \hline Average & 96.45 & 92.90 & 92.90 & 92.87 & 92.89 \\ \hline \multicolumn{6}{c}{Epoch-600} \\ \hline Class-0 & 93.30 & 89.96 & 82.40 & 86.01 & 86.10 \\ \hline Class-1 & 91.20 & 83.20 & 81.20 & 82.19 & 82.19 \\ \hline Class-2 & 95.00 & 87.31 & 93.60 & 90.35 & 90.40 \\ \hline Class-3 & 97.30 & 93.05 & 96.40 & 94.70 & 94.71 \\ \hline Average & 94.20 & 88.38 & 88.40 & 88.31 & 88.35 \\ \hline \multicolumn{6}{c}{Epoch-800} \\ \hline Class-0 & 92.80 & 89.38 & 80.80 & 84.87 & 84.98 \\ \hline Class-1 & 90.80 & 82.64 & 80.00 & 81.30 & 81.31 \\ \hline Class-2 & 94.30 & 85.87 & 92.40 & 89.02 & 89.08 \\ \hline Class-3 & 96.10 & 90.11 & 94.80 & 92.40 & 92.43 \\ \hline Average & 93.50 & 8 Figure 6: Average analysis of MDTL-ICDDC technique (**a**) epoch 200, (**b**) 400 epoch, (**c**) epoch 600, (**d**) epoch 800, (**e**) epoch 1000, and (**f**) epoch 1200. Figure 8: TL and VL analysis of MDTL-ICDDC technique. Figure 7: TA and VA analysis of MDTL-ICDDC technique. Figure 4:0 ROC curve analysis of MDTL-ICDDC technique. Figure 4:1 Precision-recall curve analysis of MDTL-ICDDC technique. Table 4 and Figure 12 demonstrate a comparative \(reca_{l}\) analysis of the MDTL-ICDDC approach with other models under distinct classes. The experimental results indicated that the Gabor and BoW-SRP models have shown lower classification results with least average \(reca_{l}\) of 62.30% and 67.85% respectively. Moreover, the BoW-LBP and GLCM-SVM models have accomplished somewhat enhanced average \(reca_{l}\) values of 74.15% and 73.52% correspondingly. Simultaneously, the GoogleNet and VGGNet techniques have resulted in reasonable average \(reca_{l}\) values of 85.26% and 82.78% respectively. But, the MDTL-ICDDC model has gained maximal average \(reca_{l}\) of 92.90%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Precision (\%)**} \\ \hline
**Methods** & **Class-0** & **Class-1** & **Class-2** & **Class-3** & **Average** \\ \hline Gabor & 57.80 & 47.80 & 56.50 & 85.20 & 61.83 \\ \hline BoW-SRP & 76.00 & 49.60 & 59.10 & 88.60 & 68.33 \\ \hline Bow-LBP & 75.80 & 55.80 & 72.20 & 94.90 & 74.68 \\ \hline GLCM-SVM & 72.70 & 70.33 & 78.42 & 80.41 & 75.47 \\ \hline GoogleNet & 74.05 & 89.38 & 78.48 & 90.01 & 82.98 \\ \hline VGGNet & 79.42 & 82.11 & 89.78 & 93.23 & 86.14 \\ \hline MDTL-ICDDC & 94.49 & 90.28 & 91.86 & 94.98 & 92.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision analysis of MDTL-ICDDC technique with existing algorithms under various classes. Figure 11: Precision analysis of MDTL-ICDDC technique under various classes. Table 5 and Figure 13 depict a comparative \(accu_{y}\) examination of the MDTL-ICDDC model with other algorithms under distinct classes.