# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 1 Introduction
employed the classical VGG-16 CNN architecture and transferred learning with X-ray images to determine the OA severity level [15]. These images were preprocessed using an SVM and Sobel edge detector in order to locate the knee joint area. Their study used X-ray images from the OAI. A set of 4446 X-rays were used in this study, representing a total of 8892 knees. When classifying the five Kellgren and Lawrence (KL) grades, they achieved an accuracy of 59.6%. Later, in another work, the same group updated the preprocessing step to use a fully convolutional neural network (FCN) to determine the bounding box of the knee joint. The FCN method was found to be highly accurate in determining regions of interest (ROI), and when combined with a CNN for classification, the method achieved an accuracy of 61.9% [16]. Unlike the two-stage frameworks developed in [15; 16], a recent work [17] proposed an end-to-end CNN architecture for knee OA severity assessment without using a neural network for preprocessing. This method used branches in its CNN that are referred to as \"attention modules\", which provide an unsupervised determination of the ROI of X-ray images. Another recent work added a long short-term memory (LSTM) classifying step following the CNN layers in their network [18]. Given the nature of LSTM for processing sequential data, additional images were generated in a preprocessing step by cropping a fixed ROI and rotating the cropped image by 5, 10, \(-\)5, and \(-\)10 degrees. The original image and augmented images were stacked, giving about 4600 images used for training and about 480 for testing. Their work also used images from the OAI and achieved an accuracy of 75.28% for the 5-category classification. It can be seen that 3D CNNs have developed quickly and are attracting interest as a method for analyzing sequences of images or other volumetric data. In a recent study, a 3D CNN was used for classifying real-world objects [19]. Depth information was used to create a 3D shape that was converted into a volumetric representation (voxels) to be classified by the 3D CNN. In addition, 3D CNNs have shown to be useful in medical image processing. When classifying lung nodules, working with 3D volumetric data in a 3D CNN outperformed 2D CNNs [20]. [PERSON] et al. applied a 3D CNN model to calculate the probability of needing a total knee replacement (TKR) within the next nine years [21]. Their work demonstrated that the automated discovery of OA biomarkers from turbo spin echo (TSE) and double-echo steady-state (DESS) images could outperform models that use only demographic and clinical data. Another work explored this area using the popular 2D U-Net architecture for the segmentation of cartilage and meniscus in the knee, which were fed into a 3D CNN for classifying the severity of the cartilage and meniscus lesions [22]. Given the large amount of volumetric data, another recent work for classifying knee lesions used cropping of 3 ROIs from knee MRI to reduce the dimensionality before processing by multiple 3D CNN [23]. Aside from these applications, 3D CNNs have also been applied to segmentation problems including knee cartilage segmentation [24] and segmentation of brain lesions [25]. For knee OA severity classification, while previous methods used 2D CNNs to analyze X-ray images, in this work we propose a method using a 3D CNN and MR images. The details of the proposed method is introduced in the next section.