Fig. 4. Evaluation of ML model performance. (a) Confusion matrices showing number of TPs, TNs, FPs and FNs for both ResNet-50 and s-Net from a representative trial (b) ML performance is evaluated by determining the accuracy, sensitivity and specificity computed on the test dataset. Results are shown for both network architectures tested (c) repeatability of the trained CNN. The network is serially trained multiple times with the same dataset; error bars indicate the standard deviation of five trials; (d) inter-trial variability of the CNN. The trained CNN is tested on multiple datasets; error bars indicate the standard deviation of three independent trials. In (c) and (d), blue corresponds to ResNet-50 and red corresponds to s-Net.