Breast UltraSound Image classification using fuzzy-rank-based ensemble network

Abstract

Breast Cancer is one of the most commonly occurring cancers in women. Detecting cancer at an earlier stage increases the chance of survival. Thus the development of an automatic CAD system is important for cancer diagnosis. Researchers have used various image modalities like mammograms and Magnetic Resonance Images to detect cancer. This manuscript uses UltraSound Images for detecting breast cancer. We have proposed a fuzzy-rank-based ensemble network for detecting breast cancer. The proposed model uses four different base learners, namely VGG-Net, DenseNet, Xception, and Inception, and it tries to take advantage of the predictions made by the base learners. The weights of the initial layers of the base learners are pre-trained on the ImageNet dataset. In contrast, the final five layers are fine-tuned using a publicly available Breast Ulta Sound Image dataset. The fuzzy rank of the base learners’ predictions is used to make the final classification. Conducting five-fold cross-validation using the base learners an accuracy of 77.69±3.22, 83.23±3.14, 78.31±2.27, and 78.62±4.23 were obtained. Furthermore, using the proposed fuzzy-rank-based model, an accuracy of 85.23±2.52 is obtained. We have proved that the proposed fuzzy-rank-based ensemble network increases the classification performance.