## 3 Results
### Knee Osteoarthritis Detection
#### 3.3.3 Comparative Analysis of Reviewed Methods and Techniques
- Among the deep learning models developed for knee OA classification, MobileNet demonstrated the most optimal performance in [47] when used as a stand-alone model, while the least optimal performance was observed in [44] using Deep Siamese ResNet34, with an accuracy of 61.71%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**References** & **Methodology** & **Architecture** & \begin{tabular}{c} **With/Wit-** \\ **Hout ROI** \\ \end{tabular} & **Classes** & **Dataset** & **Sample Size** & **Metric** \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON]. \\ et al. [35] \\ \end{tabular} } & \(\bullet\) Multi-class classification using traditional ML models and Hu’s invariant moments. & \(\circ\) KNN and Decision Tree & With & 5 & [6] & 2000 & \begin{tabular}{c} Accuracy (RNN/DT) = \\ 99.80\%/95.75 (Medical \\ expert-1) and 98.65\%/95.4 (Medical expert-II) \\ \end{tabular} \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [46] \\ \end{tabular} } & \(\bullet\) Multi-class classification using optimal features and traditional ML models. & \(\circ\) DT, RF, KNN, GB, XGB & With & 5 & \begin{tabular}{c} [PERSON] \\ and [PERSON] \\ \end{tabular} & 8660 & \begin{tabular}{c} Best model (CEGN): precision: \\ 99.43\% \\ \end{tabular} \\ \cline{2-7}  & \(\bullet\) Multi-class classification using an ensemble of deep learning models. & \(\circ\) DenseNet161, DenseNet121, ResNet43,VCG19. & Without & 5 & OAI & 9786 & \begin{tabular}{c} Best model (Ensemble): \\ Weighted kappa = 0.99\% \\ MAE = 0.0256 \\ \end{tabular} \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [48] \\ \end{tabular} } & \(\bullet\) Multi-class classification using traditional ML models and 10-fold cross-validation. & \(\circ\) KNN, SVM, Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost & Without & 4 & [PERSON] & 5778 & \begin{tabular}{c} Best model (CEGN and \\ exemplen model) \\ Accuracy: 98.90\% \\ Accuracy: 98.90\% \\ \end{tabular} \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON]. \\ et al. [49] \\ \end{tabular} } & \(\bullet\) Multi-class classification using DL model and Gradient conjugate. & \(\circ\) Artificial neural networks & \multirow{3}{*}{With} & \multirow{3}{*}{5} & \multirow{3}{*}{[6]} & \multirow{3}{*}{1650} & \multirow{3}{*}{1650} & \multirow{3}{*}{1650} & \multirow{3}{*}{1650} \\  & & & & & & & 98.2\% (surgeon-2) \\ \cline{1-1} \cline{5-7}  & & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [47] \\ \end{tabular} } & \(\bullet\) Multi-class classification using DL models. & \(\circ\) DenseNet, EfficientNetB7, Inception, MobileNet, NASNet, ResNet52V2, VCG19, Xception & Without & 5 & Local dataset & 8250 & \begin{tabular}{c} Best accuracy (MobileNet): \\ 98.36\% \\ \end{tabular} \\ \cline{1-1} \cline{5-7}  & \(\bullet\) MedKnee Multi-class classification & \(\circ\) Xception & \(5\) & Without & OAI & 5000 & Accuracy: 97.20\% \\ \cline{1-1} \cline{5-7}  & \(\bullet\) Multi-class classification using DL model. & \(\circ\) VGG, DenseNet, ResNet, and Inception V3 & & & & & \begin{tabular}{c} Best model (VCG19): \\ Accuracy = 96.7\% \\ MAE = 0.344 \\ \end{tabular} \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} [PERSON] et al. \\ [51] \\ \end{tabular} } & \(\bullet\) Multi-class classification using DL model and SW annotation. & \(\circ\) U-Net & \(5\) & With & OAI & 100 & Accuracy:96.3\% \\ \cline{1-1} \cline{5-7}  & \(\bullet\) Multi-classification and binary classifications using DL models. & \(\circ\) DenseNet169, Xception, ResNet50, DenseNet121, InceptionResNetV2, InceptionV3 & & & &
\begin{tabular}{c} Best model (DenseNet169): \\ Accuracy: 95.93\% (5-class) \\ 93.78 (2-class) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Updated artificial intelligence systems to detect knee osteoarthritis severity and progression from radiographs.