  The use of Deep Learning (DL) based methods in medical histopathology images
have been one of the most sought after solutions to classify, segment, and
detect diseased biopsy samples. However, given the complex nature of medical
datasets due to the presence of intra-class variability and heterogeneity, the
use of complex DL models might not give the optimal performance up to the level
which is suitable for assisting pathologists. Therefore, ensemble DL methods
with the scope of including domain agnostic handcrafted Features (HC-F)
inspired this work. We have, through experiments, tried to highlight that a
single DL network (domain-specific or state of the art pre-trained models)
cannot be directly used as the base model without proper analysis with the
relevant dataset. We have used F1-measure, Precision, Recall, AUC, and
Cross-Entropy Loss to analyse the performance of our approaches. We observed
from the results that the DL features ensemble bring a marked improvement in
the overall performance of the model, whereas, domain agnostic HC-F remains
dormant on the performance of the DL models.
