## 1 Introduction
In the first place, [PERSON] et al. [12] make suggestions for the knee joint area in their study. Due to the patella, they see an increased intensity, follow by a fast reduction in intensity because of the distance between the knee joints. As a next step, a linear SVM classifier is applied to the knee joint proposals using Histogram of Oriented Gradients (HoG) features [13]. Using a deep neural network (DNN), [PERSON] et al. [14] describe a new approach to finding knee joints and achieving cutting-edge performance. Semantic segmentation is the primary focus of the FCN-based approach [15]. The identification of the knee joint may benefit from a more specialized CNN detection architecture. Knee KL grade classification was proposed by [PERSON] et al. in early 2009, based on hand-crafted qualities such as texturing, Chebyshev statistic, and Haralick features [8]. A number of deep learning-based techniques have been attempted in the last three years by [PERSON] et al. [10; 15]. KL grade classification is seen as a regression problem by [10]. The mean squared loss is used to fine-tune BVLC SafeNet for knee KL grade classification. By combining cross-entropy and mean squared losses, they come up with a new CNN model and improve its performance. There is an algorithm called the Deep Siamese CNN that was created by [PERSON] and his colleagues [12]. Selecting sub-areas of the knee joint and then combining predictions from the specified regions are both performed using this tool. Figure 1: Examples of knee joints from each KL grade and their accompanying criteria. A diverse variety of visual tasks, including image classification and object detection and segmentation, may now be handled by deep learning-based algorithms [16]. Detection and segmentation of cells [17], mitosis [18], white matter lesion segmentation [19], and retinal blood vessel segmentation [19] are just a few of the applications for deep learning in medical image analysis that have popped up in the last five years. Many researchers use algorithms based on deep learning for analyzing osteoarthritis in the knee. Nevertheless, improvements must be made to knee analyses. The implementation of a more effective loss function can enhance knee KL grading performance. It is in this study that we employ Inception ResNet V2 CNN models sequentially to automatically assess knee OA severity and get the best results. After collecting the data, we initially applied pre-processing procedures. They applied novel image enhancement techniques using Open CV to further enhance the image quality of the knee X-ray dataset along with contour detection and segmentation methods to detect the region of interest (ROI) of knee joints. Then, the Inception ResNet V2 model of CNN architecture with parameter hyper-tuning and customized hidden layers with available features was applied to knee X-ray images for KL Grading. The knee KL grading challenge puts the most common CNN models to the test, and the results reveal that, using the Inception ResNet V2 model, the suggested methods provide the total knee severity grading performance. A knee X-ray image is shown in Figure 1 as part of a pipeline for rating severity. A general block diagram of the proposed technique is shown below in Figure 2.