  Continuum robots in robot-assisted minimally invasive surgeries provide
adequate access to target anatomies that are not directly reachable through
small incisions. Achieving precise and reliable motion control of such
snake-like manipulators necessitates an accurate navigation system that
requires no line-of-sight and is immune to electromagnetic noises. Fiber Bragg
Grating (FBG) shape sensors, particularly edge-FBGs, are promising tools for
this task. However, in edge-FBG sensors, the intensity ratio between Bragg
wavelengths carries the strain information that can be affected by undesired
bending-related phenomena, making standard characterization techniques less
suitable for these sensors. We showed in our previous work that a deep learning
model has the potential to extract the strain information from the full
edge-FBG spectrum and accurately predict the sensor's shape. In this paper, we
conduct a more thorough investigation to find a suitable architectural design
with lower prediction errors. We use the Hyperband algorithm to search for
optimal hyperparameters in two steps. First, we limit the search space to layer
settings, where the best-performing configuration gets selected. Then, we
modify the search space for tuning the training and loss calculation
hyperparameters. We also analyze various data transformations on the input and
output variables, as data rescaling can directly influence the model's
performance. Moreover, we performed discriminative training using Siamese
network architecture that employs two CNNs with identical parameters to learn
similarity metrics between the spectra of similar target values. The
best-performing network architecture among all evaluated configurations can
predict the sensor's shape with a median tip error of 3.11 mm.
