Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models

Invasive ductal carcinoma (IDC) breast cancer is a significant health concern for women all around the world and early detection of the disease may increase the survival rate in patients. Therefore, Computer-Aided Diagnosis (CAD) based systems can assist pathologists to detect the disease early. In this study, we present an ensemble model to detect IDC using DenseNet-121 and DenseNet-169 followed by test time augmentation (TTA). The model achieved a balanced accuracy of 92.70% and an F1-score of 95.70% outperforming the current state-of-the-art. Comparative analysis against various pre-trained deep learning models and preprocessing methods have been carried out. Qualitative analysis has also been conducted on the test dataset. After the detection of IDC breast cancer, it is important to grade it for further treatment. In our study, we also propose an ensemble model for the grading of IDC using the pre-trained DenseNet-121, DenseNet-201, ResNet-101v2, and ResNet-50 architectures. The model is inferred from two validation cohorts. For the patch-level classification, the model yielded an overall accuracy of 69.31%, 75.07%, 61.85%, and 60.50% on one validation cohort and 62.44%, 79.14%, 76.62%, and 71.05% on the second validation cohort for 4×, 10×, 20×, and 40× magnified images respectively. The same architecture is further validated using a different IDC dataset where it achieved an overall accuracy of 90.07%. The performance of the models on the detection and grading of IDC shows that they can be useful to help pathologists detect and grade the disease.