## 5 Discussion of Results and Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Input Data** & **ML/DL Methods** & **Classification Problem** & **Reference Standard** & **Results** \\ \hline \multicolumn{5}{c}{ Polar Maps (Images)} \\ \hline \multirow{4}{*}{[PERSON] et al. (2019), 2019} & \multirow{4}{*}{Upright and supine polar maps} & DL: & Presence of coronary artery stenosis \(>\)20\% & \multirow{4}{*}{Conmany Angiography} & The AUC score, specificity and sensitivity were 81\%, 83\%, and 66\% \\  & & & Max-Pooling filter) and & & \\  & & & 3 fully connected layers. & (Normal/Abnormal) & \\ \hline \multirow{4}{*}{[PERSON] et al. (2018), 2018} & \multirow{4}{*}{Raw and quantitative polar maps} & Deep CNN: & \multirow{4}{*}{Conmany Angiography} & \multirow{4}{*}{The AUC score, specificity and sensitivity were 80\%, 85\%, and 82\%} \\  & & & 3 \(\times\) (ConvLayers, Relu, & & \\  & & Max-Pooling filter), 3 fully connected layers, followed by & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2019)} & \multirow{4}{*}{Polar maps} & Graph-based CNN, & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Expert Reader} & The accuracy, specificity and sensitivity were 91\%, 96\%, and 86\% \\  & & Chebyshev & (Normal/Abnormal) & & \\ \hline \multicolumn{5}{c}{ 2D SPECT MPI images} \\ \hline \multirow{3}{*}{[PERSON] et al. (2020)} & \multirow{3}{*}{SPECT MPI images} & SVM with deep features, VGG-19 & (2 classes) & \multirow{3}{*}{Expert Reader} & The accuracy, specificity and sensitivity were 9\%, 57\%, and 100\% \\ \cline{1-1} \cline{3-5}  & & SVM with shallow features, VGG-19 & (2 classes) & \multirow{3}{*}{Expert Reader} & The accuracy, specificity and sensitivity were 94\%, 100\%, and 88\% \\ \hline \multirow{4}{*}{Proposed work} & \multirow{4}{*}{SPECT MPI images} & Transfer learning, & (2 classes) & \multirow{3}{*}{Expert Reader} & The accuracy, AUC, specificity and sensitivity were 86\%, 92\%, 71\%, and 100\% \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{4}{*}{Proposed work} & \multirow{4}{*}{SPECT MPI images} & RGB-CNN & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Expert Reader} & The accuracy, specificity and sensitivity were 93\%, 86\%, and 100\% \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 8: Cont. It is worth mentioning that only one previous work is highly related to the current research study and regards the presence of coronary artery stenosis (normal or abnormal) as a two-class classification problem. This work employed well-known CNNs to classify normal/abnormal patient cases [53], utilizing transfer learning. The authors employ deep neural networks that underwent a pre-training phase as well as an SVM classifier characterized by deep and shallow features derived from the respective networks. Most of the applied DL-based methods (AlexNet, GoogleNet, DenseNet, Resnet, VGG-16) in this dataset provided accuracies less than 87%, and only the VGG-19 utilizing SVM with shallow features increased the accuracy slightly. The knowledge-based classification model, which uses extracted features based on shapes and empirically verified parameters, fine-tuned on the training and validation images, provided the highest classification accuracy of up to 93%. Through the conducted comparative analysis of the proposed RGB-CNN method with the related ML and deep learning techniques as listed in Table 5, it is concluded that the proposed RGB-CNN model outperforms all the previous techniques in MPI imaging. It provides slightly better performance in classification accuracy (94%) and AUC score (93%), making it a competitive solution to this diagnosis task.