## 5 Results and Discussion
### Comparison with the State-of-the-Art Methods  
A comparative analysis with other state-of-the-art methods is shown in Table 5, in terms of classes, results, and the number of parameters. All compared models used a transfer-learning approach with a high number of parameters, whereas the proposed model was a lightweight customized CNN and had significantly fewer parameters.  
The authors of [29] classified 4 classes with an accuracy ranging from 61% to 99.30% for 6 different algorithms, and among them, CCT had the lowest number of parameters, at approximately 4.07 million. Our model achieved an accuracy of 99.52% with only 0.18 million parameters. Using only the axial plane for 3 different classes, the authors of [33]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & **K1** & **K2** & **K3** & **K4** & **K5** & **K6** & **K7** & **K8** & **K9** & **K10** & **Avg (\(\mu\pm\sigma\))** \\ \hline \multirow{4}{*}{Pre} & Cyst & 99 & 99 & 99 & 100 & 100 & 100 & 100 & 100 & 99 & 98 & 99.4 \(\pm\) 0.66 \\ \cline{2-11}  & Normal & 100 & 100 & 97 & 100 & 100 & 100 & 100 & 99 & 100 & 100 & 99.5 \(\pm\) 0.95 \\ \cline{2-11}  & Stone & 100 & 100 & 98 & 100 & 100 & 100 & 100 & 99 & 100 & 100 & 99.7 \(\pm\) 0.64 \\ \cline{2-11}  & Tumor & 100 & 100 & 95 & 100 & 100 & 100 & 100 & 100 & 100 & 98 & 99.3 \(\pm\) 1.55 \\ \hline \multirow{4}{*}{Rec} & Cyst & 100 & 100 & 99 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.9 \(\pm\) 0.30 \\ \cline{2-11}  & Normal & 100 & 100 & 98 & 100 & 100 & 100 & 100 & 100 & 99 & 99.7 \(\pm\) 0.64 \\ \cline{2-11}  & Stone & 99 & 99 & 90 & 100 & 100 & 100 & 100 & 99 & 99 & 93 & 97.9 \(\pm\) 3.30 \\ \cline{2-11}  & Tumor & 100 & 100 & 96 & 100 & 100 & 100 & 100 & 99 & 100 & 100 & 99.5 \(\pm\) 1.20 \\ \hline \multirow{4}{*}{Fsc} & Cyst & 100 & 100 & 99 & 100 & 100 & 100 & 100 & 100 & 100 & 99 & 99.8 \(\pm\) 0.40 \\ \cline{2-11}  & Normal & 100 & 100 & 97 & 100 & 100 & 100 & 100 & 100 & 100 & 99.7 \(\pm\) 0.89 \\ \cline{2-11}  & Stone & 100 & 99 & 94 & 100 & 100 & 100 & 100 & 99 & 97 & 98.8 \(\pm\) 1.83 \\ \cline{2-11}  & Tumor & 100 & 100 & 96 & 100 & 100 & 100 & 100 & 100 & 99 & 99.5 \(\pm\) 1.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: K = 10-fold average result of the proposed model in (%): Precision (Pre), Recall (Rec), F1-score (Fsc).  
Figure 11: Confusion matrix for 10 th fold.  
implemented 2 transfer-learning algorithms with approximately 6 million parameters. Both the algorithms achieved an approximately 85% precision and F1-score, whereas our model achieved more than 99%. With the same 3 different kidney planes for 4 classes, 99.44% was achieved by the authors of [30]. They used a DenseNet algorithm, and their model had 19.82 million more parameters than our model. Though the results obtained by VGG-DN-KNN using 5-fold cross-validation was 100% in the work performed by the authors of [31], it only achieved approximately 96% for VGG16-NB and Densenet121-KNN. All the algorithms implemented had a high number of parameters. The author of [32] applied XResNet50 to coronal images and achieved only 96.82% with 23.7 million parameters, whereas the proposed model had only 0.18 million parameters and achieved 99.52%.