# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 4 Discussion  
The contributions of this study are two-fold. Firstly, a practical DL framework has been proposed to detect abnormal parathyroid scintigraphy images of patients with suspected HTTP. The efficiency of the presented ParaNet topology reached approximately 95% in detecting images with abnormal PGs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & **ACC** & **SEN** & **SPE** & **PPV** & **NPV** & **F1** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline E-L & 90.0 & 88.7 & 93.3 & 97.0 & 76.7 & 92.7 \\ L-Tc04 & 89.0 & 87.8 & 92.2 & 96.6 & 75.1 & 92.0 \\ E-Tc04 & 41.1 & 48.0 & 23.9 & 61.3 & 15.5 & 53.9 \\ E-L-Tc04 & 94.8 & 93.8 & 97.2 & 98.8 & 86.2 & 96.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results when supplying ParaNet with image pairs. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Patients} & \multicolumn{8}{c}{**Medical Experts**} & \multicolumn{8}{c}{**ParaNet**} \\ \hline \multirow{2}{*}{Patients} & No & TP & FN & FP & SEN & \
\begin{tabular}{c} FPR \\ (\%) \\ \end{tabular} & TP & FN & FP & SEN &
\begin{tabular}{c} FPR \\ (\%) \\ \end{tabular} \\ Primary HPPT & 448 & 400 & 48 & 0 & 89.3 & 0 & 376 & 72 & 0 & 83.9 & 0 \\ Secondary/tertiary HPPT & 17 & 17 & 0 & 0 & 100 & 0 & 17 & 0 & 0 & 100 & 0 \\ Total & 465 & 417 & 48 & 0 & 89.7 & 0 & 393 & 72 & 0 & 84.5 & 0 \\ Abnormal PGs & & & & & & & & & & & \\ Solitary Neoplasm & 368 & 324 & 44 & 19 & 88.0 & 5.5 & 291 & 77 & 79 & 79.1 & 21.4 \\ Multi-glandular disease & 256 & 160 & 96 & 4 & 62.5 & 2.4 & 131 & 125 & 7 & 51.2 & 5.1 \\ Total & 624 & 484 & 140 & 23 & 77.6 & 4.5 & 422 & 202 & 86 & 67.6 & 16.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Sentigraphic results according to the medical experts’ diagnosis and the ParaNet model in 256 operated patients with positive surgical findings in the neck. Secondly, the Grad-CAM algorithm is successfully employed to assist human experts in explaining the model's decisions. However, an extensive evaluation on a PG-level basis revealed that the model could not correctly identify the factual findings' actual location while producing several false positive findings. Furthermore, an extensive evaluation on a PG level basis revealed that the model exhibited lower sensitivity than the experts in the whole study cohort (83.1%) and surgically confirmed cases (67.6% vs. 77.6% of the experts) while producing several false positive findings (18.2% and 16.9% vs. 4.5% of the experts). The findings of this study revealed that deep networks might yield remarkable accuracy and minimum losses in terms of metrics, but their proper understanding may be limited. False positive reduction is necessary to improve the diagnostic efficiency of the model and is a matter of future research. The sub-optimal specificity caused by the overwhelming number of FP findings can be explained by two decisive factors constraining the model's learning capacity. Firstly, there is a substantial data imbalance issue. Normal scans are under-represented (28.48%) in the dataset, thereby introducing susceptibility to biased training and results. Data augmentation has reduced the effect of this issue in model training. However, the imbalance issue remains and may not be circumvented entirely. Secondly, the efficiency of the Grad-CAM algorithm is questionable in a variety of cases, as reported in the literature [25]. More specifically, Grad-CAM may fail to recognise multiple findings of the same class in the same image. In addition, grad-CAM may poorly visualise the exact location of the important features on some occasions. Therefore, future research involves employing more sophisticated approaches, such as the Grad-CAM++ algorithm [25]. Nevertheless, the actual agreement with the human expertise reached an acceptable rate (76.5% agreement on a PG level and roughly 95% on a patient level). The study suffers some limitations. Firstly, the study employed state-of-the-art models solely.