## 6 Conclusions and Forthcoming Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Studies** & **Datasets** & **X-Ray** & **MRI** & **CT Scan** \\ \hline S17 & Tested reports of [PERSON] patients & No & 70\% & No \\ S18 & 35 MRI images & No & 86\% & No \\ S19 & Dataset of 18 images & No & 91\% & No \\ S20 & (1) 36 BNC samples, (2) 40 control samples & No & 97\% & No \\ S21 & Knee MRI reports & No & 95\% & No \\ S22 & X-ray images of patients & 50\% & No & No \\ S23 & (1) 21 images for training patella, (2) 48 images as the validation set & 80\% & No & No \\ S24 & 432 images of the femur & 97.2\% & No & No \\ S25 & 75,000 patients’ data & 95\% & No & No \\ S26 & 24 X-ray images & 50\% & No & No \\ S27 & 1024 X-ray images & 82.98\% & No & No \\ S28 & Micro CT trabecular datasets & No & No & 65\% \\ S29 & Multidetector Computed Tomography (MDCT) dataset & No & 98\% \\ S30 & 10 knees-experienced patterns & No & No & 90\% \\ S31 & Seven samples of pig femur bone & No & No & 65\% \\ S32 & CT scan images of patients & No & No & 70\% \\ Total Accuracy & \multicolumn{2}{c}{75.9\%} & 87.8\% & 77.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Accuracy of machine learning papers. learning techniques are best for X-ray, MRI, and CT scan images for knee bone diseases. Undoubtedly, techniques of deep and machine learning are both helpful in medical image processing, but it is the situation that decides which is the best technique to obtain the best accuracy. As it is a comparative systematic literature review, we conclude by finding and calculating each study's accuracy. The explosion of computerized medical service information has prompted a flood of information-driven clinical exploration dependent on machine learning. As of late, as an influential strategy for big data, deep learning and information retrieval have increased a focal situation in machine learning circles for incredible points of interest in inclusion portrayal and example acknowledgment. Many articles present a far-reaching outline of studies that utilize big data and information retrieval strategies to manage clinical information. Initially, because of the investigation of the attributes of clinical information, different kinds of clinical information (e.g., clinical pictures, clinical notes, lab results, indispensable signs, and segment informatics) are discussed, and the subtleties of some open clinical datasets are mentioned. Furthermore, a short review of normal deep learning models and their qualities is performed. At that point, considering the wide scope of clinical exploration and the considerable variety of information types, a few big data applications for clinical information are shown: assisted conclusions, anticipation, early admonition, and different errands. Even though there are difficulties in applying deep learning procedures to clinical information, it is as of yet advantageous to anticipate a promising future for deep learning applications in clinical big data toward precision medicine [49]. Our research is still in progress; as many studies are performed in the medical field by using big data and information retrieval techniques, we have a plan to review big data and information retrieval techniques for this literature review to make this study more qualified. Table 11 contains the citation's references of selected studies. By using this table, reader can find the reference of selected studies from bibliography.