GC-EnC: A Copula based ensemble of CNNs for malignancy identification in breast histopathology and cytology images

Abstract

In the present work, we have explored the potential of Copula-based ensemble of CNNs(Convolutional Neural Networks) over individual classifiers for malignancy identification in histopathology and cytology images. The Copula-based model that integrates three best performing CNN architectures, namely, DenseNet-161/201, ResNet-101/34, InceptionNet-V3 is proposed. Also, the limitation of small dataset is circumvented using a Fuzzy template based data augmentation technique that intelligently selects multiple region of interests (ROIs) from an image. The proposed framework of data augmentation amalgamated with the ensemble technique showed a gratifying performance in malignancy prediction surpassing the individual CNN’s performance on breast cytology and histopathology datasets. The proposed method has achieved accuracies of 84.37%, 97.32%, 91.67% on the JUCYT, BreakHis and BI datasets respectively. This automated technique will serve as a useful guide to the pathologist in delivering the appropriate diagnostic decision in reduced time and effort. The relevant codes of the proposed ensemble model are publicly available on GitHub.