## 1 Introduction  
An estimated one in three persons will suffer from knee osteoarthritis (OA) at some point in their lives [1; 2]. More than half of the people 65 and older have signs of OA, including at that one joint. More than a quarter of Americans will reach the age of 65 by 2030, putting them at risk of developing OA [3]. Knee osteoarthritis (OA) has a significant impact on the quality of life of old age people. Even worse, no treatment has emerged to date which can reduce or completely stop the advancement of knee OA's degenerative structural changes. OA patients' quality of life can be improved and slowed down by early detection and treatment, but there are hazards involved. OA of the knee is characterized by Joint Space Narrowing (JSN), subchondral sclerosis, and the development of osteophytes. Knee joints' 3D architecture can be seen in MRI images. For regular knee OA diagnosis, MRI is unsuitable since it is only available at big medical centers and is costly. X-rays have long been considered the gold standard for diagnosing osteoarthritis of the knee due to their high levels of reliability, low cost, and ubiquitous availability. According to WHO guidelines, the Kellgren and Lawrence (KL) severity grading system was adopted in 1961 [4]. It is divided into five classes, from 0 to 4, according to the KL system's knee OA severity. For each grade level, the data are shown in Figure 1, along with the students who met the criterion for inclusion. In most cases, doctors examine a scanned X-ray image of the knees and quickly assign a KL grade to each joint. The accuracy of a doctor's diagnosis is heavily dependent on his or her expertise and attention to detail. Furthermore, the KL grading standard is rife with ambiguity. For example, the criteria for KL grade 1 include the possibility of osteophytic lipping and a low probability of JSN. Even the same doctor may award a separate KL grade to the same knee joint at various periods. A study by [PERSON] et al. [5] found a KL interrater reliability range of 0.67-0.73. Because of the ambiguous nature of the KL grade criterion, we believe that physicians' grading of the knee joint is not as reliable as it could be [6]. The grade of a knee joint may be incorrectly changed from a lower one (for example, from grade 1 to grade 2) to a higher one (e.g., from grade 1 to grade 4). The correctness of grading should be the only criterion for evaluation. Mean absolute error (MAE), which is modelled by MAE used in age estimate assessments, is employed as a substitute metric for knee KL grade categorization evaluation. [7]. Because knee OA is so common, it is vital to precisely identify it and determine how severe it is. Full-automation knee severity grading does not become fatigued over a long evaluation period and may offer objective, reliable projections. One must first recognize the knee joint in a raw X-ray picture and then categorize it into one of five KL categories in order to effectively predict the severity of knee osteoarthritis (OA) based just on the knee joint. In recent years, knee joint recognition and KL grade classification algorithms have been created. In order to recognize knee joints, [PERSON] et al. [8] employ a template matching technique and a sliding window strategy. Images of 20 knee joints of 15 \(\times\) 15 pixels each are moved to the present X-ray picture, and the Euclidean distances between them are calculated. It is determined that the knee joint is located at the lowest Euclidean distance possible by looking at the window [9]. A linear support vector machine (SVM) is also employed by [PERSON] et al. [10] to identify knee joints by using Sobel vertical image gradients as features, generated by horizontal edges in knee joint photos. By using entire radiographs as input, [PERSON] et al. developed an end-to-end easily understandable model that is as accurate as musculoskeletal radiologists and does not necessitate any human image pre-processing steps. Data relevant to the treatment of patients were used to make the model's predictions [11].