Breast Cancer Image Multi-Classification Using Random Patch Aggregation and Depth-Wise Convolution based Deep-Net Model

Abstract

Adapting the profound, deep convolutional neural network models for large image classification can result in the layout of network architectures with a large number of learnable parameters and tuning of those varied parameters can considerably grow the complexity of the model. To address this problem a convolutional Deep-Net Model based on the extraction of random patches and enforcing depth-wise convolutions is proposed for training and classification of widely known benchmark Breast Cancer histopathology images. The classification result of these randomly extracted patches (size 50X50X3) is aggregated using majority vote casting in deciding the final image classification type. It has been observed that the proposed Deep-Net model implementation results when compared with classification results of the VGG Net (16 layers) learned features, outclasses achieving accuracy up to 89.6% on multi-class classification for 40X magnified images. The results further indicate model trained for images of one optical magnification factor (eg. 40X) might not classify images captured on different magnification (like 100X, 200X, and 400X) with similar accuracy. Thus, different classifiers are required at different magnifications.