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Deep learning (DL) architectures have established state-of-the-art capabilities across numerous fields. Unpacking their internal operations and reasoning paths is critical due to their high architectural complexity and opaque, black-box character, particularly in high-stakes fields like healthcare. Visual analytics (VA) integrated with DL methodologies are frequently employed to extract data insights, yet they regularly face challenges related to visual clutter (VC). This research introduces a streamlined neural network (NN) view design engineered to mitigate visual clutter while clarifying DL model elements for field specialists and everyday users. We applied clustering techniques to aggregate hidden neurons according to similarities in their activation profiles. This framework accommodates both macroscopic and micro-level perspectives of the generated neuron groups. Utilizing a tabular medical dataset as a practical case study, this clustered visualization strategy successfully decreased visual clutter by 54% for neuron graphics and by 88.7% for their interconnections, while successfully exposing correlated neuron activations developed throughout model training.