## 3 Results
### Knee Osteoarthritis Detection  
Recent literature shows that deep learning and traditional machine learning can reliably assess knee osteoarthritis severity according to the Kellgren-Lawrence (KL) scale. This semi-quantitative scoring system varies from grade KL0 (no osteoarthritis) to grade 4 (severe osteoarthritis). It has been accepted by the World Health Organization (WHO) as a reference for epidemiological studies on osteoarthritis since 1961. Knee osteoarthritis classification is mainly associated with joint space narrowing (JSN), osteophytes, sclerosis, and bone extremity deformation. JSN is measured between the femur and the tibia in the medial compartment. It is associated with articular cartilage and subchondral bone sclerosis and is widely used as the main indicator of knee osteoarthritis. Figure 2 shows the stages of knee osteoarthritis severity according to KL's scoring system with joint space narrowing estimation as mentioned in [6]. Other scoring systems have been used to assess the severity of KOA, such as the Osteoarthritis Research Society International (OARSI) atlas [14] and the International Knee Documentation Committee (IKDC) scoring system [9]. [PERSON] et al. [25] have used the Visual Analogue Scale (VAS) and Hospital for Special Surgery (HSS) scores. [PERSON] et al. [26] used the Western Ontario and McMaster Universities (WOMAC) scores and Knee Outcomes in Osteoarthritis Scores (KOOS). Other studies have developed their models of KOA detection using both the KL and WOMAC scoring systems [27] or by grouping KL0 and KL1 in a single category (no OA or normal) [13; 28; 29], KL2, KL3, and KL4 as abnormal for binary classification [14; 15; 20; 30; 31; 32], and KL3 and KL4 in severe osteoarthritis [24; 29; 33; 34] for 3-class and 4-class classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & & **Method** & **Dataset (X-ray Images)** & **Metric** \\ \hline [PERSON] et al. [18] & \(\bullet\) & YOLOv5 & 400 X-ray images from OAI & Recall = 93\%. \\ [PERSON] et al. [6] & \(\bullet\) & Active contour algorithm +Otsu & Local dataset: 1650 X-ray images & Not available \\  & & + Sobel and Prewitt & 1650 X-ray images & \\  & \(\bullet\) & Fuzzy C-means, k-means & \\ [PERSON] and [PERSON] [19] & clustering, Center rectangle, Region-Based Active Contour, and Seed Point Selection & Local dataset of 25 X-ray images & Best models: Center rectangle, Seed point selection: SP = 100\%, SE = 0\% \\ [PERSON] et al. [20] & \(\bullet\) & YOLOv3 Gaussian and Canny filters & OAI & Train accuracy = 97\%, mAP = 97\%, IoU = 85\% \\ [PERSON] et al. [15] & \(\bullet\) & BoneFinder + Adaptive ROI Segmentation using superpixel labeling and k-means clustering & OAI + MOST & Not available \\  & \(\bullet\) & KNEEL: Knee Anatomical Landmark Localization with hourglass networks & 370 X-ray images from Oulu University Hospital, Finland & Precision (resolution = 2.5) = 93.48\% \(\pm\) 0.44 \\ [PERSON] et al. [21] & \(\bullet\) & JC-Regnet based on VGG + AMSCRad & OAI & Accuracy = 96\%, recall = 0.92 \\ [PERSON] and [PERSON] [22] & \(\bullet\) & YOLOv2 (Image size: 256 \(\times\) 320 pixels) & OAI & Mean Jaccard index = 0.858, recall = 92.2 \\ [PERSON] et al. [23] & \(\bullet\) & ResNet18 using global average pooling layer & OAI & IoU = 0.86 \\ [PERSON] et al. [24] & \(\bullet\) & HRNet: Detection of knee position, RetinaNet & OAI & Not available \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of the main ROI detection methods used in the selected articles.