## 4 Discussion  
DL can substantially contribute to medical image processing and medical image analysis for diagnostic purposes. In the case of CAD, the significance of DL in the analysis of MPI SPECT scans and MPI SPECT Polar Maps is already highlighted [13; 17; 22; 26; 29; 30]. In this study, we proposed an advanced deep learning (DL) methodology that incorporated feature-fusion and attention modules to enhance the localisation abilities of the baseline VGG19 network. Additionally, we implemented the Grad-CAM++ algorithm to enable a visual assessment of the regions where VGG19 focused on its most critical features. We initiated our discussion by examining the agreement between AFF-VGG19 and the medical experts. We trained the model using the human reader's diagnostic yield as a reference to achieve this. The results demonstrated a high level of agreement, with an impressive 89.92% alignment between the model's predictions and the classifications assigned by the human reader to the images. This finding underscored the model's capacity to capture relevant features and make clinically meaningful predictions, providing valuable insights for medical practitioners. Furthermore, we performed training and assessment using the ICA findings as a reference, simulating real-world diagnostic scenarios. AFF-VGG19 displayed a robust diagnostic strength, boasting an accuracy of 0.78881, closely mirroring the performance of the human expert (0.7840). Notably, the model exhibited more balanced sensitivity (0.8732) and specificity (0.7216) rates, outperforming the human expert in this regard (0.9577 and 0.6484, respectively). Human readers, particularly nuclear medicine physicians, may exhibit bias towards the positive class of CAD due to several reasons. First, they may encounter cases with ambiguous diagnostic test results, where the findings are not definitive, leading them to err on the side of caution and lean towards diagnosing CAD to avoid missing potential cases, even if the evidence is inconclusive. Second, there may be a preference for false-positive results, because they allow for follow-up with invasive procedures like invasive coronary angiography (ICA) to confirm the presence of CAD. Missing CAD cases (false negatives) can be risky for patients, and this preference for false positives may contribute to the bias towards the CAD class. Additionally, the bias can be related to the dataset used, as nuclear medicine physicians typically select symptomatic patients for SPECT/MPI testing in clinical practice. As a result, the dataset used for training and validation may have a higher proportion of patients with CAD, reflecting the real-world scenario where doctors encounter more patients with cardiac symptoms. To mitigate the impact of bias, training the machine learning model on a balanced dataset during the training and validation phases is essential. A balanced dataset allows the model to handle both positive and negative cases effectively without becoming skewed towards the majority class. Nevertheless, despite using a balanced dataset, the model may still face challenges in real-world scenarios due to inherent biases in data collection and human decision-making. Therefore, evaluating the model on diverse and unbiased datasets is crucial to ensure its generalisability and reliability. Moreover, raising awareness among human experts about potential biases and encouraging them to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Polar Maps**} & **Number of** & **Number of** & **Number of** & **Number of** \\  & **Examined** & **Adequate** & **Ambiguous** & **Irrelevant** \\  & & **Explanations** & **Explanations** & **Explanations** \\ \hline _TP_ cases & 45 & 43 & 1 & 1 \\ _TN_ cases & 35 & 34 & 1 & 0 \\ _FP_ cases & 12 & 0 (4 *) & 0 & 12 (8 *) \\ _FN_ cases & 8 & 0 & 0 & 8 \\ Total & 100 & 77 & 2 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the examination of the produced Grad-CAM++ heatmaps.