## 4 Discussion  
Previous studies by the author group established DL methods for identifying and localising abnormal PG in scintigraphic images [22; 23]. The conception of these methods was based on recent literature reviews [30]. However, despite the remarkable performance metrics in distinguishing between normal and abnormal patient cases, previous approaches yielded many false-positive PGs, which were revealed by investigating the sensitivity maps produced by Grad-CAM.  
The present study improves the algorithm for false-positive reduction and more precise localisation. Based on the conception that an abnormal PG can be identified using low-level features, usually extracted by the first convolutional layers of a CNN, the study proposes modifications in the baseline ParaNet to allow for more local feature extraction. Local, low-level, and high-level features are now extracted in a non-sequential manner and are fused. The study employed a post hoc explainability method (Grad-CAM++) to localise the DL-suggested abnormal PGs. Grad-CAM++ produced the sensitivity maps, highlighting the areas of the image where the model grounded its predictions. As a result, ParaNet++ showed better classification accuracy, obtaining 0.9868 on a patient basis and when using the expert's diagnostic yield as the reference. Scrutiny on a PG-level basis revealed an 88.31% agreement between the model and the experts. Hence, the latter classification accuracy measured the agreement between DL and the physicians. In addition, there was a significant reduction in false positives compared to our previous work [22].  
The proposed network performs well on external testing involving surgically verified samples. Though the network's training and initial validation had been performed using human interpretation as the reference, its generalisation capability is adequate to classify 99% of the parathyroidectomy-labelled images correctly, which is considered among the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Mean** & **Standard Deviation** \\ \hline Accuracy & 0.9859 & 0.0043 \\ t-value & & \(-\)1.25969 \\ Outcome & At the 0.05 level, the population mean (0.9859) is not significantly different from the test mean (0.9861) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Statistical significance test for the evaluation of the model’s stability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Time (s)** \\ \hline Complete training (no cross-validation) & 869 \\ Complete 10-fold cross-validation & 8759 \\ Prediction of class (single image) & 0.4 \\ Visualisation (Grad-CAM++) (single image) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Operation times.  
main strengths of the framework. On a PG-level basis, the model showed 87.3% sensitivity in detecting abnormal PGs versus 83.9% by the experts.  
Direct comparisons with related works may be inconclusive due to variations in the test data, the image acquisition devices, and the data sizes. Nevertheless, the available comparisons populate Table 12.  
The superiority of ParaNet+ compared to the baseline ParaNet is verified. ParaNet achieved an accuracy of 0.948 on a patient-level basis, while ParaNet+ reached 0.98681. The research's results are also consistent with the literature [22; 32; 33; 34].