Analysis of Dimensional Influence of Convolutional Neural Networks for Histopathological Cancer Classification

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 a 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 involve 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 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.