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

Adapting the profound, deep convolutional neural network models for large image classification can result in layout of architectures with a number learnable parameters and tuning those varied considerably grow complexity model. To address this problem Deep-Net Model based on extraction random patches enforcing depth-wise convolutions is proposed training widely known benchmark Breast Cancer histopathology images. The these aggregated using majority vote casting deciding final type. It has been observed that model implementation results when compared VGG Net(16 layers) learned features, outclasses terms accuracy applied to breast tumor Histopathology objective work examine comprehensively analyze sub-class performance across all optical magnification frontiers.