## 6 Conclusions and Forthcoming Work  
The objective of this research was to conduct a comparative \"systematic literature review\" on MRI, CT scan, and X-ray images of knee bone by using deep learning techniques. Thirty-two papers associated with deep learning and machine learning approaches were reviewed in this research, which provide the accuracy mentioned in Tables 17 and 18, respectively. Let us discuss the outcomes of each research paper used for this research to finalize our results. The following points describe the outcomes of each paper:  
**S1:** This paper is about deep learning techniques with X-ray images of the knee bone disease osteoarthritis. The authors showed a novel methodology for naturally diagnosing and reviewing knee OA from X-rays. It can help patients experiencing knee pain to obtain a quicker finding. Their technique accomplishes the best multi-class grouping results, despite having an alternate testing set: a normal multi-class precision of 66.71%, radiographical OA AUC of 0.93, quadratic weighted Kappa of 0.83, and MSE of 0.48. It may be possible to contrast this with normal human understandings. A total of 3000 subjects from the Osteoarthritis Initiative dataset was used in their research. **S2:** This paper covers the knee bone tumor X-rays with deep learning techniques. The ImageNet dataset was used for this paper. In this paper, the researchers present a successful end-to-end deep learning model for knee bone tumor arrangement. In future work, the researchers will grow the present model to confine the tumor's position, which may altogether support clinical treatment. The proposed new model uses semi-supervised ensemble Wnet (SSEW) in their research. They used three models--InceptionV3, ResNet50, and MobileNet. The accuracy results for the proposed model (SSEW) are 97.64 for Two-Class, 79.23 for \"79.23\", and 80.31 for Five-Class, respectively. **S3:** This paper is about studying the osteoarthritis of knee bones via X-rays with deep learning techniques. The Kellgren-Lawrence classification model is used for research. The accuracy of this model is, for moderate OA, 91.5%; for minimal OA, 80.4%; and for doubtful OA, 57%. **S4:** This paper is related to X-ray images of knee bone osteoarthritis with deep learning techniques. This paper researched a few new techniques for the programmed measurement of knee OA seriousness, utilizing the CNN model. In the future, there is a plan to increase the accuracy of the CNN model of the knee joint by applying more techniques. The accuracy of this technique was 94.2%. **S5:** The researchers worked on knee osteoarthritis in X-ray images of knee bone. The active contour segmentation technique was used for this research. A total of 200 X-ray images were used as the dataset. The accuracy of this technique was 87.92%. **S6:** In this paper, X-ray images of osteoarthritis patients were used with deep learning techniques. Two algorithms were used, which are the K-nearest Neighbor Algorithm (KNN) and Supports Vector Machine (SVM). The accuracy results for these techniques were, for normal images, KNN = 100% and SVM = 79% and for abnormal images, KNN = 100% and SVM = 100%. **S7:** This research paper is about MRI images of knee bones for the detection of knee bone diseases from MRI. Online MRI datasets were used for this technique. Scale Space Local Binary Pattern (LBP) feature extraction was used for this research. The accuracy attained in this research was 96.1%. **S8:** In this paper, the convolutional neural network (CNN) was used for MRI images with deep learning techniques. The MICCAI dataset was used to accomplish this research. In this paper, \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **X-ray** & **MRI** & **CT Scan** \\ \hline Deep Learning & 87.7\% & 91.8\% & 86.24\% \\ Machine Learning & 75.9\% & 87.8\% & 77.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Accuracy. the researchers built up a knee ligament division calculation from a high goal MR volume utilizing a novel completely 3D Convolutional Neural Network (CNN).