## 3 Results
### Knee Osteoarthritis Detection
#### 3.3.1 Deep Learning Models
The proposed model was trained for 30 epochs using a batch size of 24 images and stochastic gradient descent (SGD). The approach yielded an average accuracy of 71.74%. [PERSON] and [PERSON] [37] employed a deep convolutional neural network to predict knee osteoarthritis using Alexnet. In this approach, 7434 knee radiographic images were obtained from the Kaggle platform and divided into training and testing datasets, with a ratio of 70% to 30%, respectively. The model comprises five convolution layers. The initial convolutional layer comprises 96 filters, the second layer 256 filters, and a block of three convolutional layers with 384 filters, separated by a max-pooling layer. The fifth convolution layer is followed by a third max-pooling layer and two fully connected layers (FC) with 4096 neurons, which are then followed by a softmax layer. The model achieved an accuracy of 98%. [PERSON] et al. [38] built DenseNet169 from scratch with 169 layers to diagnose knee osteoarthritis severity according to the KL classification system using radiographic images. In this model, each pair of layers was connected in such a way that contours and primitive shapes could be used directly in the fully connected layer. The final layer was modified to have five outputs, corresponding to the KL grading system. In this study, 40,280 X-ray images were collected and augmented from the Osteoarthritis Initiative (OAI) dataset and divided into three subsets for model training, validation, and testing. The training set consisted of 32,116 images, the validation set included 4074 images, and the testing set comprised 4090 images. The model achieved an F1 score of 0.866 and an accuracy of 0.872. [PERSON] and [PERSON] [14] developed a method based on the combination of two convolutional neural networks to diagnose the existence of knee osteoarthritis (KL \(\geq\) 2). Each model comprises a first convolutional layer that has been pre-trained on ImageNet and a second part consisting of seven independent fully connected layers (FC), corresponding to each of the KL grades and six OARSI grades (femoral osteophytes, tibial osteophytes, and narrowing of joint space for lateral and medial compartments, respectively). The initial convolutional block of the first model was constructed using ResNet50 following the evaluation of ResNet18 and ResNet34. In contrast, the initial convolutional block of the second model was developed using ResNeXt, and the squeeze-excitation block (SE) was incorporated into the second part. Moreover, the global average pooling technique was employed to establish a connection between the two models (ResNet50-SE and ResNeXt-SE). Both models were trained on 19,704 X-ray images from the OAI dataset and tested on 11,743 X-ray images from the MOST dataset using the optimizer Adam and a dropout of 0.5 inserted before each fully connected (FC) layer. In the initial epoch, the FC layer of each model was trained with a learning rate of 0.01. In the second and third epochs, a learning rate of 0.001 was employed, and subsequently, the learning rate was set to 0.0001 in the fourth epoch. This method achieved an average accuracy of 0.98 for binary classification of knee osteoarthritis from X-ray images, as well as a Cohen kappa coefficient of 0.79, 0.84, 0.94, 0.83, 0.84, and 0.90 for the six OARSI grades, respectively. In 2018, [PERSON] et al. [39] employed a deep Siamese network model comprising two branches of ResNet34. The model was trained on 18,376 images from the MOST dataset, then validated on 2957 images, and finally tested on 5960 images from the OAI dataset. In this approach, the image was divided into lateral and medial compartments, with each part of the image serving as the input for a branch of the model. Each branch comprises three blocks of a convolution layer, a max-pooling layer, a linear unit rectified layer (ReLU), a normalization batch layer (Conv-BN-ReLU), followed by a global average pooling layer, two blocks of (Conv-BN-ReLU), and a softmax layer.