## 3 Results
### Knee Osteoarthritis Detection
#### 3.3.3 Comparative Analysis of Reviewed Methods and Techniques  
This section presents a comparative analysis of the different diagnostic approaches used in the papers cited to diagnose knee osteoarthritis. The classification of knee osteoarthritis is conducted in two stages. The first stage involves the processing of input images. In contrast, the second stage entails extracting characteristics of the region of interest to assess the severity of knee osteoarthritis according to different grades (KL). The initial stage of the process is conducted with or without the detection of a region of interest (ROI), while the subsequent stage involves the automated extraction of features and classification using traditional machine learning algorithms and deep learning methods. **A. **: In the initial phase of the knee osteoarthritis identification process, studies employing methodologies that did not require ROI localization utilized databases of readily available images of individual knees as input to the proposed model. Furthermore, in studies based on ROI detection, manual selection or additional deep learning models for localization are employed before the classification process. Both types of studies, with and without ROI detection, employ pre-processing of the input images with resizing according to the requirements of the models used. As demonstrated in Table 6, the methods that employed ROI detection exhibited a slight advantage over the other methods, with the highest accuracy observed in [43] at 99.81%. In this study, the edges of the knee bone are first detected and then filtered to suppress noise without losing essential image information. In the second step, the region of interest (ROI) was identified based on pixel density. Finally, the traditional machine learning K-nearest neighbor (KNN) classifier was constructed to classify knee arthrosis. In [44], the manual selection of the ROI before the classification of knee osteoarthritis did not yield satisfactory results. In this approach, the lowest accuracy among studies employing ROI detection was obtained with an average multi-class accuracy of 61.71%. - A brief analysis of Table 6 allows us to conclude that approaches that did not employ ROI detection achieved performances comparable to those obtained with methods developed with ROI localization. The best performances were obtained in [28] with a weighted kappa coefficient of 0.99 and MAE of 0.0256 using a set of deep learning models. The approach used in [45] exhibited the lowest performance, with a balanced accuracy of 64.13 \(\pm\) 0.88. This approach employed a Siamese network and a semi-supervised learning technique. B. In the second phase of feature extraction and classification of knee OA, the most successful techniques were based on additive methodologies for extraction, including cartilage thickness estimation in [6] and Hu invariant moments used in [43]. - The traditional machine learning models XGB and KNN have shown performance as potential extractors and classifiers of knee osteoarthritis, giving the best results when compared with other models with accuracies of 99.81%, and 99.43% in [43], and [46], respectively. - Table 6 reveals that the common methodologies used in the different approaches are the use of a multitude of models, with the selection of the optimal model or the adoption of the result obtained by the ensemble of models. The DenseNet and ResNet deep learning models are the most commonly adopted in the proposed approaches, their performance being compared with that of other models. However, they are not the optimal candidates for this purpose. The DenseNet model achieved the best performance in the study [34], using DenseNet169, with an accuracy of 95.93%. In contrast, the ResNet model demonstrated the best performance in [47], using ResNet152V2, with an accuracy of 95.88%.