## 4 SLR Conduct
### Data Extraction  
Tables 9 and 10 provide the information extraction frame that is utilized for all the chosen essential investigations to do a top-to-bottom study. It describes the author's name of the paper, published year, image type, model, data set, disease, and accuracy of the proposed techniques. Tables 11 and 12 describe the work of the researchers that they have performed by using different methodologies in their research papers, predictions of their future work, and the advantages of their research. Tables 13 and 14 show the gaps in the review articles on deep learning and machine learning papers. Figure 5: Published years of selected ML papers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**\#** & **Author** & **Year** & **Image Type** & **Data Set** & **Model** & **Disease** & **Accuracy** \\ \hline
**S1** & “[PERSON]. [PERSON], [PERSON] et al.” & 2018 & X-ray &
\begin{tabular}{c} 3000 subjects from the \\ Osteoarthritis \\ Initiative dataset. \\ \end{tabular} & Deep Siamese Convolutional & Osteoarthritis & Accuracy = 0.93, AUC = 0.93, \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline
**S2** & “[PERSON]. [PERSON] et al.” & 2018 & X-ray & \begin{tabular}{c} ImageNet dataset \\ \end{tabular} & \
\begin{tabular}{c} Proposed new model \\ semi-supervised ensemble \\ Wnet (SSEW) \\ \end{tabular} & Knee Bone Tumor &
\begin{tabular}{c} Two-class = 97.64\% \\ Fourth-class = 79.23\% \\ Five-class = 80.31\% \\ \end{tabular} \\ \hline
**S3** & “[PERSON] et al.” & 2009 & X-ray & \begin{tabular}{c} 350 images of X-ray \\ \end{tabular} & \begin{tabular}{c} Kellgren-Lawrence \\ classification \\ \end{tabular} & \
\begin{tabular}{c} Osteoarthritis \\ \end{tabular} &
\begin{tabular}{c} For moderate OA = 9.15\%, for \\ minimal OA = 80.4\% for \\ doubtful OA = 57\% \\ \end{tabular} \\ \hline
**S4** & “[PERSON] et al.” & 2016 & X-ray & \
\begin{tabular}{c} Bilateral PA fixed Plexion \\ knee X-ray images \\ \end{tabular} &
\begin{tabular}{c} Convolutional Neural \\ Networks (CNN) \\ \end{tabular} & Knee & 94.2\% \\ \hline
**S5** & “[PERSON] et al.” & 2016 & X-ray & \
\begin{tabular}{c} 200 datasets of knee \\ X-ray images \\ \end{tabular} &
\begin{tabular}{c} The active contour \\ segmentation technique \\ \end{tabular} & Osteoarthritis & 87.92\% \\ \hline
**S6** & “[PERSON] et al.” & 2019 & X-ray & X-ray images of OA patients & \
\begin{tabular}{c} K-Nearest Neighbor \\ Algorithm (KNN) Support \\ Vector Machine (SVM) \\ \end{tabular} & Osteoarthritis &
\begin{tabular}{c} For normal images \\ (KNN = 100\%, SVM = 79\%) \\ For abnormal images \\ (KNN = 100\%, SVM = 100\%) \\ \end{tabular} \\ \hline
**S7** & “[PERSON] et al.” & 2018 & MRI & Online MRI dataset & \begin{tabular}{c} Scale Space Local Binary \\ Pattern (LBP) feature \\ extraction, SVM \\ \end{tabular} & \
\begin{tabular}{c} Detection of knee \\ bone from MRI \\ Images \\ \end{tabular} &
\begin{tabular}{c} Accuracy = 96.1\% \\ MCC rate = 88.26\% \\ \end{tabular} \\ \hline
**S8** & “[PERSON] et al.” & 2018 & MRI & MICCAI datasets &
\begin{tabular}{c} CONVOLITIONAL \\ NEURAL NETWORKS \\ (CNN) \\ \end{tabular} & Osteoarthritis & 87.7\% \\ \hline
**S9** & “[PERSON]. [PERSON] et al.” & 2019 & MRI & \
\begin{tabular}{c} 3D MRI datasets SK10 \\ Dataset OAI Imorphics \\ Dataset OAI ZIB \\ \end{tabular} & 3D CNNs, SSMs & Osteoarthritis &
\begin{tabular}{c} 3D MRI datasets \\ SK10 = 75.73\%, Dataset OAI \\ Imorphics = 90.4\%, Dataset \\ OAI ZIB = 98.5\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Extracted data for knee bone-related MRI, CT scans, and X-rays using deep learning techniques.