## 6 Conclusions and Forthcoming Work
The accuracy of this technique was 87.7%. The accuracy of the proposed system will be improved in the future. **S9:** In this paper, 3D MRI datasets SK10, Dataset OAI Imorphics, and dataset OAI ZIP were used for MRI images of knee bones with deep learning techniques. The models used for this research were the 3D CNN and SSM. The accuracies of these models were for the 3D MRI datasets SK110, 75.73%; for the dataset OAI Imorphics, 90.4%; and for the dataset OAI ZIb, 98.5%. **S10:** In this research 3D MRI dataset images were used for knee bone MRI reports. The researchers of this research created and assessed a programmed 3D deformable methodology for knee MRI having force inhomogeneity. The exploratory outcomes showed that their methodology accomplished 95% Dice, 93% SENS, and 99% SPEC in the volume assessment but an ASSD of 1.17 mm and RMSD of 2.01 mm in the surface assessment. **S11:** In this research, the Convolutional Neutral Network (CNN) model was used for 175 patient MRI datasets for knee bones. The accuracy attained in this research was 91%. **S12:** This research is on CT scan image reports on knee bone. It is a systematic literature review. In this research, the datasets of CT scan, and some of MRIs, are used. Their results show that 95% accuracy can be achieved. **S13:** In this study, 2D CT scan images were used for knee joint reconstruction. The CT scan data of multiple patients were used for this research. The researchers of this research used a convolutional neural network for all knee bone diseases. A dataset of 21 abdominal CT scans images was used to accomplish this research. The accuracy obtained from the research was 93%. **S14:** The CT scan data of multiple patients was used by using different deep learning models. The technique models used to accomplish this research were Finite element (FE), Sobel Operator, Laplacian of Gaussian operator, and Canny edge detection. The researchers claim that 83% accuracy can be achieved for CT scan images of the knee by using these techniques. **S15:** The researchers of this research used the CT scan images of 59 patients by using the CNN technique for sclerotic metastasis of knee bone CT scans. The researchers created a two-layered coarse-to-fine course structure to initially working with an exceptionally delicate hopeful age framework with the greatest affectability of \(\sim\)92% but with a high FP level (\(\sim\)50 per understanding). The average accuracy gain with this technique was 70% to 80%. **S16:** In this study, the researchers used the 3D image datasets of knee bone CT scans. The convolutional neural network was used for their research. In this paper, the researchers proposed a novel structure to determine seven anatomical milestones of the distal femur bone. They attained 80% accuracy in this research. **S17:** In this study, the researchers worked on MRI images of [PERSON]'s patients' reports using machine learning techniques. The technique that was used to accomplish this research is the General Linear Model (GLM). This study's motivation was to build up a strategy to measure the seriousness of bone ailments in type 1 GD patients and to differentiate between various GD genotypes and between GD patients and healthy people. The accuracy obtained with this technique for MRI images of knee bones was 70%. **S18:** In this study, the researchers performed work on MRI images of osteoarthritis and osteochondral patients. The machine learning technique used for this study was Pattern Recognition and Multivariable regression (WND-CHARM). WNDCHRM might be an effective technique for the grouping of ligament MRIs. A dataset of 35 MRI images was used to accomplish this study. The accuracy obtained by this technique for MRI images of knee bones was 86%. **S19:** In this study, the researchers studied the programmed characterization of finished tissues in 3D MRI. This research used the complex magnetic resonance signal, which provides segmented information. This research is good for the feature extraction of knee bone for all diseases.