Table 7. Confusion matrix.

True positive rate (Recall/Sensitivity): It implies how the caries lesion is accurately detected when it is present there.	Sensitivity is expressed as ${\displaystyle \frac{TP}{TP+FN}}$
True negative rate (Specificity): That is the amount of negative caries lesion examination when there's no affected lesion.	Specificity is measured as ${\displaystyle \frac{\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{P}}}$
Dice Coefficient: This metric measures between two samples.	It is defined as ${\displaystyle \frac{\left(2\left|\mathrm{A}\cap \mathrm{B}\right|\right)}{\left(\left|\mathrm{A}\right|+\left|\mathrm{B}\right|\right)}}$, where |A| and |B| are the number of elements in the sample.
Accuracy: It can be defined as the percentage of correctly classified instances.	It is calculated as ${\displaystyle \frac{\mathrm{T}\mathrm{P}+\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{P}+\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{N}+\mathrm{F}\mathrm{P}}\ast 100}$.
Precision: It explains the pureness of our positive detections efficiently compared to the ground truth.	It is the positive predictive value defined as ${\displaystyle \frac{\mathrm{T}\mathrm{P}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{P}}}$
F-Score: The F-score is a process of combining the model's precision and recall and the harmonic mean of the model's precision and recall.	It is expressed as $2\times {\displaystyle \frac{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\times \mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}+\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}}$