  As deep neural networks include a high number of parameters and operations,
it can be a challenge to implement these models on devices with limited
computational resources. Despite the development of novel pruning methods
toward resource-efficient models, it has become evident that these models are
not capable of handling "imbalanced" and "limited number of data points". We
proposed a novel filter pruning method by considering the input and output of
filters along with the values of the filters that deal with imbalanced datasets
better than others. Our pruning method considers the fact that all information
about the importance of a filter may not be reflected in the value of the
filter. Instead, it is reflected in the changes made to the data after the
filter is applied to it. In this work, three methods are compared with the same
training conditions except for the ranking values of each method, and 14
methods are compared from other papers. We demonstrated that our model
performed significantly better than other methods for imbalanced medical
datasets. For example, when we removed up to 58% of FLOPs for the IDRID dataset
and up to 45% for the ISIC dataset, our model was able to yield an equivalent
(or even superior) result to the baseline model. To evaluate FLOP and parameter
reduction using our model in real-world settings, we built a smartphone app,
where we demonstrated a reduction of up to 79% in memory usage and 72% in
prediction time. All codes and parameters for training different models are
available at https://github.com/mohofar/Beta-Rank
