GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many computer-aided diagnostic systems have been developed for the detection of cancer. These systems have quite lower performance, so more accurate diagnosis is the need of the time to save the life of human beings. For large and imbalanced image datasets, efficient learning of the network is very important to detect and classify breast cancer more accurately. In this paper, a graph based adaptive regularized learning of deep network (GARL-Net) is proposed for more accurate breast cancer classification. Transfer learning is used for training the backbone network DenseNet121. furthermore, fine tuning of backbone network is followed by the estimated improved loss function. The improved loss function is actually graph based adaptively regularized complement cross entropy loss. The SoftMax cross entropy in itself is not sufficient to classify image samples accurately, so complement entropy technique is incorporated with the cross-entropy loss to overcome the misclassification issue. Further, by adaptive scaling of regularization term with spatial graph Laplacian basis used to adaptively penalize the complement cross entropy loss for improving the learning of the network. The performance of the proposed method is evaluated using BreakHis and BACH 2018 histopathology image datasets and outperforms the existing state-of-the-art methods and achieved 99.00% of precision, 99.40% of recall, 99.20 % of F-1score, and 99.49% of accuracy for binary classification of breast cancer image samples of BreakHis dataset.

Automated summary

Breast cancer is a fatal disease faced by women globally, due to lack of awareness and convenient diagnostic systems. This paper proposes a graph-based adaptive regularized learning of deep network (GARL-Net) for more accurate breast cancer classification. The proposed method outperforms existing state-of-the-art methods with high precision, recall, F-1 score, and accuracy.