## 3 Results
### Knee Osteoarthritis Detection
#### 3.3.1 Deep Learning Models  
In recent years, the use of deep learning methods for disease detection has attracted the attention of the research community due to their effectiveness in solving complex problems. In the field of deep learning, Convolutional Neural Networks (CNNs) have transformed visual analysis. Their capability to extract intricate patterns and features from images has made CNNs crucial for tasks such as image classification. [PERSON] et al. [35] developed an end-to-end supervised learning model of a Deep Convolutional Neural Network (DCNN) for automatic diagnosis of KOA. The model was optimized with the hyperparameters and built with five convolution blocks. Each block includes a Re-LU layer and a max-pooling layer to extract the distinctive characteristics of joint space narrowing and to evaluate osteophytes. Then, a flatten layer is followed by a fully connected layer, and a drop-out layer (0.1) is used to avoid over-fitting. Finally, the Adam optimizer and the cross-entropy function were used to obtain the loss value with a learning rate of 0.001. The model was trained in 30 epochs with a batch size of 100 images. The proposed model achieved an accuracy of 77.24%. Figure 2: Radiological stages of knee osteoarthritis according to the cartilage thickness [6] and Kellgren and Lawrence (KL) grading system. [PERSON] et al. [16] have noted that when classifying knee osteoarthritis using loss of Cross-Entropy (CE), two neighboring grades may be confused because cross-entropy disregards the proximity of classified objects. To solve this problem, an adjustable ordinal loss was applied to various models of DenseNet, VGG, ResNet, and InceptionV3. The proposed adjustable ordinal loss is obtained by designing a square matrix of order 4, represented by the penalty weights between the predicted grade j and the real grade i, with i and j among 0 and 4, in which the penalty weight of each KL grade to itself is set to 1 and the highest KL grades if the grades are distant. Applying this ordinal loss slightly improved classification accuracy and reduced MAE error. VGG19 achieved the highest accuracy, 69.6% using ordinal loss and 69.3% using cross-entropy. When the region of interest (ROI) was automatically detected, the accuracy of VGG19-ordinal was improved to 70.4%. In their study, [PERSON] et al. [18] employed the VGG16 model for the preliminary identification of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. The model was selected following training and evaluation of VGG16-BN, ResNet152, DenseNet201, and VGG16 using 8892 knee X-ray images from the OAI dataset. In this study, the dataset was randomly split into a 7:1:2 ratio corresponding to training, validation, and test sets. The region of interest (ROI) was then detected using the You Only Look Once (YOLOv5) algorithm. The optimal results were achieved through the transfer learning of VGG16, which involved the addition of two fully connected layers (FC) with 4096 units and a third FC layer with 1000 units, followed by a softmax layer. VGG16 achieved an accuracy of 69.8%, while DenseNet-201 and VGG16-BN yielded 68.5% and 68.2%, respectively. [PERSON] et al. [36] have proposed an automated system to identify knee osteoarthritis from radiographic images, called High-Resolution Network (HRNet), in conjunction with a Convolutional Mass Attention Module (CBAM). In this study, the cross-entropy loss function has been replaced by an ordinal loss function based on an ordinal matrix, as illustrated in [16]. The aim of this ordinal loss function is to penalize the misclassification of a distant KL score more than that of a neighboring score. The diverse array of layers within the HRNet framework enables the integration of both high- and low-resolution data, as well as the concurrent processing of multiple resolutions. The approach was developed by collecting 8260 radiographic knee images from the OAI dataset, which were divided into a training set (70%), a validation set (10%), and a test set (20%).