  Convolutional Neural Networks (CNNs) have revolutionized image classification
by extracting spatial features and enabling state-of-the-art accuracy in
vision-based tasks. The squeeze and excitation network proposed module gathers
channelwise representations of the input. Multilayer perceptrons (MLP) learn
global representation from the data and in most image classification models
used to learn extracted features of the image. In this paper, we introduce a
novel aggregated multilayer perceptron, a multi-branch dense layer, within the
Squeeze excitation residual module designed to surpass the performance of
existing architectures. Our approach leverages a combination of squeeze
excitation network module with dense layers. This fusion enhances the network's
ability to capture channel-wise patterns and have global knowledge, leading to
a better feature representation. This proposed model has a negligible increase
in parameters when compared to SENet. We conduct extensive experiments on
benchmark datasets to validate the model and compare them with established
architectures. Experimental results demonstrate a remarkable increase in the
classification accuracy of the proposed model.
