## 6 Conclusions and Forthcoming Work
In this study, the Support Vector Machine (SVM) was used by using a dataset of 18 MRI images. A 91% accuracy was obtained by using this technique. **S20:** In this paper, 36 BNC samples and 40 control samples of osteoarthritis patients' MRI images were used. A multivariable Support Vector machine was used for this study. The researchers examined whether multivariate help vector machine examination would allow enhanced tissue portrayal. SVM examination performed utilizing certain parameter combinations displayed especially ideal grouping properties. The outcomes show the capacity of multivariate examination to incredibly increase the MRI appraisal of ligament network status in essential scientific studies. The research proves that 97% accuracy can be achieved for MRI images of knee bones using the SVM technique. **S21:** In this research, the natural language processing framework was prepared, in an attempt to master ordered knee MRI reports from two noteworthy human services associations. The researchers assessed the execution of the framework both inside and across associations. The Support Vector Machine was used to accomplish this study. The results of this research prove that 95% accuracy can be achieved by using this technique for knee MRI reports. **S22:** This paper tried to distinguish the edges of restorative pictures, especially knee osteoarthritis pictures in various basic states utilizing the Sobel administrator and proposed developed Sobel calculation. The proposed algorithm is very good for blurred and distorted images. The researchers used the dataset of X-ray images of osteoarthritis patients by using Sobel Edge Detector Operator. Their research concludes that 50% accuracy can be obtained by using this machine learning technique. **S23:** In this paper, the researchers proposed a model-guided milestone limitation strategy for programmed ISR estimation. At first, the proposed technique required building a patella to demonstrate a factual patella shape and power data from preparatory information. They used 21 X-ray images for training patellae and 48 X-ray images as the validation data set. A component point extraction calculation was intended to determined the underlying model position naturally. The precision of the proposed technique was proved by both quantitative and subjective tests. Their research's outcome is that 80% accuracy can be achieved by using this technique. **S24:** This paper exhibited a researcher's exploration of the recognition of bone breaks in X-ray images. A dataset of 432 images of the femur was used with the Support Vector Machine and Bayesian Classifier techniques. Their research concludes that 97.2% accuracy can be achieved through these machine learning techniques. **S25:** In this study, the researchers used a dataset of 75,000 patients. They used the X-ray images of osteoarthritis patients by using the Bootstrap Aggregation technique. They achieved 97% accuracy for X-ray images of knee bone osteoarthritis. **S27:** This investigation presented a robotized computer-aided diagnostic approach for the identification of OA, which utilizes a mix of standardizations given presciently, displaying MLR utilization and highlight extraction utilizing ICA. The classification rates of the proposed CAD are higher than those obtained in previous studies. They used 1024 X-ray images of osteoarthritis patients and used the Multivariate Linear Regression (MLR) technique. They achieved 82.98% accuracy. **S28:** This research study performed a morphometrical comparison between CT scan databases of miniaturized objects by using the two-division procedure. The researchers used the Micro CT-Scan trabecular datasets by using Chan-Vese and Fixed Threshold techniques. They obtained 60% to 70% accuracy with these techniques. **S29:** This study is related to CT scan images of osteoporosis, which is a knee bone disease. They used the Multidetector Computed Tomography (MDCT) dataset of CT scan images of knee bones. They obtained 98% accuracy.