Explainable Soft Attentive EfficientNet for breast cancer classification in histopathological images

Abstract

Breast Cancer (BC) is believed to be the cancer that occurs most frequently in women worldwide, taking the lives of it’s the victims. In early diagnosis aids the patients to survive under greater probability. Several existing studies utilize diagnostic mechanisms via histopathology image for early identification of breast tumors. However, it increases the medical costs and consumes the time. Thus, in order to accurately classify the breast tumor, this study suggests a novel explainable DL technique. Using this technique, better accuracy is achieved while performing classifications. Improved accuracy may greatly help the medical practitioners for classifying breast cancer effectively. Initially, adaptive unsharp mask filtering (AUMF) technique is proposed to remove the noise and enhance the quality of the image. Finally, Explainable Soft Attentive EfficientNet (ESAE-Net) technique is introduced to classify the breast tumor (BT). Four explainable algorithms are investigated for improved visualizations over the BTs: Gradient-Weighted Class Activation Mapping (Grad-CAM) Shapley additive explanations (SHAP), Contextual Importance and Utility (CIU), and Local Interpretable Model-Agnostic Explanations (LIME). The suggested approach uses two publicly accessible images of breast histopathology and is carried out on a Python platform. Performance metrics such as time complexity, False Discovery Rate (FDR), accuracy, and Mathew’s correlation coefficient (MCC) are examined and contrasted with traditional research. In the experimental section, the proposed obtains an accuracy of 97.85% for dataset 1 and accuracy of 98.05% for dataset 2. In comparison with other existing methods, the proposed method is more efficient while using ESAE-Net for classifying the Breast cancer.