  Convolutional Neural Networks can be designed with different levels of
complexity depending upon the task at hand. This paper analyzes the effect of
dimensional changes to the CNN architecture on its performance on the task of
Histopathological Cancer Classification. The research starts with a baseline
10-layer CNN model with (3 X 3) convolution filters. Thereafter, the baseline
architecture is scaled in multiple dimensions including width, depth,
resolution and a combination of all of these. Width scaling involves
inculcating greater number of neurons per CNN layer, whereas depth scaling
involves deepening the hierarchical layered structure. Resolution scaling is
performed by increasing the dimensions of the input image, and compound scaling
involves a hybrid combination of width, depth and resolution scaling. The
results indicate that histopathological cancer scans are very complex in nature
and hence require high resolution images fed to a large hierarchy of
Convolution, MaxPooling, Dropout and Batch Normalization layers to extract all
the intricacies and perform perfect classification. Since compound scaling the
baseline model ensures that all the three dimensions: width, depth and
resolution are scaled, the best performance is obtained with compound scaling.
This research shows that better performance of CNN models is achieved by
compound scaling of the baseline model for the task of Histopathological Cancer
Classification.
