## 5 Discussion of Results and Conclusions
Following the process of rigorously exploring possible hyperparameters and regularization methods of the proposed RGB-CNN architecture, the best overall classification accuracy for the deep network model (best RGB-CNN) was established (see Figures 11-13). Authors selected the RGB-CNN model with 5 convolutional layers, batch size = 16, dropout = 0.2 and 64-64 dense nodes as the simplest and most optimum performed CNN, concerning testing accuracy and loss. Moreover, from the results above, it appears that the best RGB-CNN model is characterized by an overall classification accuracy of 93.47% \(\pm\) 2.81% when the produced overall test loss is approximately 0.18 (see Figure 12). To lay emphasis on the classification performance of the CNN approaches presented in this study, the authors followed a comparative analysis between the proposed RGB-CNN model and other SoA CNNs, commonly used for image classification problems, with reference to accuracy and other metrics such as the AUC score. Regarding the produced AUC value for the RGB-CNN models and the other SoA CNNs, as depicted in Figure 13, RGB-CNN seems to have the highest AUC score, making it possibly the best classifier in terms of performance for the given problem. The average run time of the best architecture for the proposed model is 1125 s which is considered fast for such types of networks. Similar to the other CNN-based methods, this method presents faster run time as shown in the previous works of the same team of authors [33; 56] in the case of bone scintigraphy. The results indicate that the proposed RGB-CNN is an efficient, robust and straightforward deep neural network able to detect perfusion abnormalities related to myocardial ischemia and infarction on SPECT images in nuclear medicine image analysis. It was also demonstrated that this is a model of low complexity and generalization capabilities compared to the state-of-the-art deep neural networks. Moreover, it exhibits better performance than the SoA CNN architectures applied in the specific problem regarding accuracy and AUC values. The proposed CNN-based classification approach can be employed in the case of SPECT-MPI scans in nuclear cardiology and can support CAD diagnosis. It can as well contribute as a clinical decision support system in nuclear medicine imaging. To sum up, among the major differences of RGB-CNN compared to other conventional CNNs are (i) their ability to efficiently train a model considering a small dataset without the need to undergo network pre-training with ImageNet dataset, (ii) their ability to be optimized through an exploratory analysis which helps to avoid overfitting and generalize well to unknown input images, and (iii) their less complex architecture which enhances their performance in an efficient run time [33; 57]. Regarding the limitations presented in previous studies, the models proposed in this work do not depend on specific characteristics like gender and camera specifications that can elevate the number of inputs [34]. In addition, they can perform sufficiently, even when not many training images are available. Among the privileges the proposed models enjoy is their ability to use SPECT images as input without the need for any additional data. This feature is rather distinguishing between this work and other studies. Finally, less experienced physicians can improve their diagnostic accuracy by supporting their opinion with the results of such systems. However, there are some limitations that need to be considered in future work. These are (i) the limited number of normal cases in the dataset, making it unbalanced, and (ii) the disregard of clinical and other functional data in the classification process, which would improve the diagnosis. According to the overall results of this study, the proposed deep learning structures of RGB-CNN are accredited for being extremely efficient in classifying SPECT MPI scans in nuclear medicine.