  Convolutional Neural Networks need the construction of informative features,
which are determined by channel-wise and spatial-wise information at the
network's layers. In this research, we focus on bringing in a novel solution
that uses sophisticated optimization for enhancing both the spatial and channel
components inside each layer's receptive field. Capsule Networks were used to
understand the spatial association between features in the feature map.
Standalone capsule networks have shown good results on comparatively simple
datasets than on complex datasets as a result of the inordinate amount of
feature information. Thus, to tackle this issue, we have proposed ME-CapsNet by
introducing deeper convolutional layers to extract important features before
passing through modules of capsule layers strategically to improve the
performance of the network significantly. The deeper convolutional layer
includes blocks of Squeeze-Excitation networks which use a stochastic sampling
approach for progressively reducing the spatial size thereby dynamically
recalibrating the channels by reconstructing their interdependencies without
much loss of important feature information. Extensive experimentation was done
using commonly used datasets demonstrating the efficiency of the proposed
ME-CapsNet, which clearly outperforms various research works by achieving
higher accuracy with minimal model complexity in complex datasets.
