## 4 Results
### Results from Deep Learning Architectures Applying Transfer Learning and Comparative Analysis  
In this subsection, the second deep learning classification approach of CAD patients using transfer learning was implemented, followed by a comparative analysis. Following the process discussed in Section 2.4, transfer learning was utilized employing several pre-trained CNN models, avoiding training a new network with randomly initialized weights. In this way, the classification process of SPECT MPI scans is faster and more efficient due to the limited number of training images. This approach includes efficient state-of-the-art CNNs in the medical image analysis domain, which were mainly reported in previous studies in similar classification tasks. In particular, for the purpose of this research work, certain SoA CNN architectures such as: (i) VGG16 [39], (ii) DenseNet in [43], (iii) MobileNet [59], and (iv) Inception V3 [60] were used. Concerning the training characteristics of this approach, the stochastic gradient descent with momentum algorithm was used, and the initial learning rate was set to 0.0001. It is worth mentioning that an exploratory analysis for the SoA CNNs [25,33] was previously conducted in the reported literature, paying particular attention to overfitting avoidance [62]. Overfitting is a common issue in most state-of-the-art CNNs that work with small datasets; thus, a meticulous exploration with various dropout, dense layers  
Figure 10: Diagnostic performance of RGB-CNN model in SPECT MPI interpretation assessed by ROC analysis for CAD patients. ROC = receiver operating characteristic, AUC = area under the curve, PPV = positive predictive value, NPV = negative predictive value. Figure 9: Precision curves for best RGB-CNN model showing (**a**) accuracy and (**b**) loss. and batch sizes was applied to avoid it. Overall, the CNN selection and optimization of the hyperparameters was performed following an exploration process considering a combination of values for batch-size (8, 16, 32, 64, and 128), dropout (0.2, 0.5, 0.7 and 0.9), flatten layer, number of trainable layers and various pixel sizes (200 \(\times\) 200 \(\times\) 3 up to 350 \(\times\) 350 \(\times\) 3). Moreover, a divergent number of dense nodes, like 16, 32, 64, 128, 256 and 512 was explored. The number of epochs ranged from 200 up to 500. The best-performing CNN models in terms of accuracy and loss function in the validation phase were selected as the optimum for classifying the test dataset [24; 56]. After the extensive exploration of all the provided architectures of popular CNNs, the authors defined the optimum values for the respective models' parameters, as follows:  
* VGG16: pixel size (300 \(\times\) 300 \(\times\) 3), batch size = 32, dropout = 0.2, Global Average Poolong2D, dense nodes 64 \(\times\) 64, epochs = 400, (average run time = 1853 s),
* DenseNet: pixel size (250 \(\times\) 250 \(\times\) 3), batch size = 8, dropout = 0.2, Global Average Poolong2D, dense nodes 16 \(\times\) 16, epochs = 400, (average run time = 2074 s),
* MobileNet: pixel size (250 \(\times\) 250 \(\times\) 3), batch size = 8, dropout = 0.2, Global Average Poolong2D, dense nodes 32 \(\times\) 32, epochs = 400, (average run time = 3070 s),
* InceptionV3: pixel size (300 \(\times\) 300 \(\times\) 3), batch size = 8, dropout = 0.2, Global Average Poolong2D, dense nodes 256 \(\times\) 256, epochs = 400, (average run time = 1538 s). Concerning the dropout value, 0.2 was selected as the best-performed for the investigated CNN configurations, according to the exploration process. The testing image dataset was used to evaluate the network's performance; however it is not involved in the training phase. The results of the explored SoA CNN architectures proposed in the second approach are compared to the best-performed RGB-CNN model. They are gathered in the following three figures. More specifically, Figure 11 depicts the classification accuracy in validation and testing phases for the best-performed deep learning architectures.