## 4 Discussion  
In the previous section, it was shown that the best classification accuracy is achieved using the CNN-based approach with the RiIG-based WCP images. A comparison with other works is presented in Table 8. The work of [PERSON]. [PERSON] et al. [5] yielded an accuracy of 94% with an F1 score of 0.942 using Database-I. [PERSON] et al. [20] also used the same Database-I and reported an accuracy of 94.5% with an F1 score of 0.945. Therefore, the accuracy level obtained by the proposed method using the same Database-I about 98.25% with an F1 score of 0.982 is significantly better. In another work, [PERSON] et al. [21] used the Database-II and reported an accuracy of 94.8%. [PERSON] et al. [22] reported an accuracy of 84.5% using the same Database-II combined with other databases. [PERSON] et al. [23] reported an accuracy of 85.3% with an F1 score of 0.765 using Database-II. [PERSON] et al. [24] illustrated an accuracy of 94.48% with an F1 score of 0.942 using Database-II. On the other hand, the proposed method using Database-II gives an accuracy of 98.35% with an F1 score of 0.984, which is significantly better. The method of [PERSON] et al. [43] provides accuracies of 91%, with an F1 score of 0.87 with a CNN, and 90%, with an F1 score of 0.83 using a Random Forest classifier for the Database-III. [PERSON] et al. [44] reported an accuracy of 94.62% with an F1 score of 0.911, using the same Database-III. In contrast, the accuracy and F1 score for the proposed method are superior. Furthermore, the proposed CNN-based approach is applied for classification on the Database-III with 80% training and 20% testing ratio with the same validation approach as in [43; 44]. This experiment provides an accuracy of 96.45%, a sensitivity 93.09%, a specificity 98.14% with an F1 score of 0.  
+++ ==WARNING: Truncated because of repetitions==
946, still superior to those of [43; 44]. The box plots given in Figure 10 indicate the comparison of accuracies of Table 8; these also indicate a consistent performance by the various methods including the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (Year)** & **Major Contribution** & **Database** & **Classifier** & **Performance (Accuracy in \%)** \\ \hline [PERSON], (2019) [5] & Gray level concurrency matrix (GLCM) algorithm & Database-I [26] & SVM & ACC: 94\%, F1 Score: 0.942 \\ [PERSON] (2021) [20] & Simple Convoluted Neural Network & Database-I [26] & CNN & ACC: 94.5\%, SEN: 94.9\%, SPEC: 94.1\%, F1 Score: 0.945 \\ [PERSON], (2020), [21] & Portable device-based CNN architecture & Database-II [27] & CNN & ACC: 94.8\% \\ [PERSON], (2019) [22] & Neural Network with R-CNN and ResNet-101 & Database-II [27] & R-CNN & ACC: 84.5\% \\ [PERSON], (2019) [23] & US to RGB Conversion and fine-tuning using back-propagation & Database-II [27] & VGG19 CNN & ACC: 85.3\%, SEN: 79.6\% \\ [PERSON], (2019) [24] & back-propagation & & SPEC: 88\%, F1 Score: 0.765 \\ skip connections. & Database-II [27] & Deep CNN & ACC: 94.48\%, SEN: 95.65\% \\ [PERSON], (2021) [43] & Automatic Machine Learning model (AutoML Vision) & Database-III [28] & CNN & ACC: 91\%, SEN: 82\% \\  & Learning model (AutoML Vision) & & SPEC: 96\%, F1 Score: 0.87 \\  & & Random & ACC: 90\%, SEN: 71\% \\  & & Forest & SPEC: 100\%, F1 Score: 0.83 \\ [PERSON], (2020) [44] & CNN includes VGGNet, ResNet, and DenseNet. & Database-III [28] & Deep CNN & ACC: 94.62\%, SEN: 92.31\% \\ Proposed Method & WCP Image, Custom made CNN architecture & Database-I [26] & Deep CNN & SPEC: 95.60\%, F1 Score: 0.91\% \\  & & Database-II [27] & Deep CNN & ACC: 98.25\%, SEN: 98.11\% \\  & & Object & SPEC: 98.59\%, F1 Score: 0.984 \\  & & Database-III [28] & Deep CNN & ACC: 98.55\%, SEN: 98.21\% \\  & & Object & SPEC: 98.89\%, F1 Score: 0.986 \\ \hline \hline \end{tabular}
\end{table}
Table 8: A comparison of selected studies with the proposed classification scheme using databases I, II, and III.  
Figure 10: Comparison of accuracies of the three databases from Table 8.