## 4 Results and Discussion
### Experimental Results  
The early phase involved applying the suggested CNN Inception ResNet v2 model to the KOA original dataset. As discussed earlier, after 27 epochs were executed, thenthe training accuracy was found as 78.65%, validation accuracy was 61.45%, and testing accuracy was found 72.03%. The confusion matrix is shown in Figure 9.  
Table 2 shows the classification report of the model executed on the original KOA dataset. In the second stage, the proposed CNN Inception ResNet v2 model was applied to an enhanced image obtained using image sharpening techniques discussed in Section 2. When the same Inception ResNet v2 model was applied to this new dataset of enhanced images, then training accuracy was found as 95.35%, validation accuracy was 74.58%, and testing accuracy was 91.03%. The confusion matrix is shown in Figure 10 and the classification report analysis in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Minimal & 0.88 & 0.81 & 0.85 & 29 \\ \hline Healthy & 0.59 & 0.86 & 0.70 & 23 \\ \hline Moderate & 0.55 & 0.43 & 0.48 & 15 \\ \hline Doubtful & 1.00 & 0.42 & 0.59 & 31 \\ \hline Severe & 0.78 & 1.00 & 0.88 & 8 \\ \hline Accuracy & - & - & 0.72 & - \\ \hline \end{tabular}
\end{table} 
Table 2: Confusion Matrix of Model. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Minimal & 0.90 & 0.87 & 0.89 & 22 \\ \hline Healthy & 0.78 & 0.91 & 0.90 & 19 \\ \hline Moderate & 0.88 & 0.85 & 0.91 & 6 \\ \hline Doubtful & 1.00 & 0.89 & 0.92 & 5 \\ \hline Severe & 0.89 & 1.00 & 0.88 & 7 \\ \hline Accuracy & - & - & 91.03 & - \\ \hline \end{tabular}
\end{table} 
Table 3: Classification Report on Image Enhancement Model. Figure 9: Confusion Matrix on Original KOA Dataset. Figure 11a,b represents the accuracy and loss curves of the model before image enhancement, and Figure 12a,b represents the accuracy and loss curves of the model after image enhancement techniques. Figure 11: The model before image enhancement techniques; (**a**) Training and Validation Loss; (**b**) Training and Validation Accuracy. Figure 12: The model after image enhancement techniques; (**a**) Training and Validation Loss; (**b**) Training and Validation Accuracy. Figure 10: Confusion Matrix on Image Enhanced Model. As shown in Figure 11a, the training loss and validation loss is approximately 12%, training accuracy is 62%, and validation accuracy is 46%. These results are drawn on our model with the original images of KOA datasets. The testing accuracy was found as 72%. As we had applied the image sharpening techniques for image enhancement and then applied the same model on enhancement images, we found the training loss to be around 8% and accuracy of training was 87.5%, validation accuracy was 80%, and testing accuracy was 91.03%. Figure 12a,b represents the same respectively. Classification techniques for knee OA severity and their performance matrices are shown in Table 4. We compared our results with those of prior studies that used deep learning to categorize the severity of knee OA and used VGGNet, ResNet, and DenseNet as the CNN architectures. In the past, researchers have only had a moderate degree of success when attempting to categorize the severity of knee OA, with the maximum reported accuracy being 71.97%. On the other hand, our approach using the image enhancement methodology yielded a comparable result with less data and achieved an accuracy of 91.03% when using CNN Inception ResNet v2. [26]. According to the comparative analysis of the contribution of our research work, after pre-processing, image segmentation, and image sharpening of the KOA dataset, the results have increased from 72% to 91.03% in terms of overall accuracy using the Inception ResNet V2 model. From the comparison made among the proposed image enhancement methodologies using the Image ResNet V2 model, we conclude that:  
* Images are processed by well before Images ResNet V2 CNNs for a specific dataset.