Breast cancer diagnosis based on detecting lymph node metastases using deep learning

Abstract

Introduction: Automated detection of metastatic breast cancer from whole slide images of lymph nodes utilizing a deep convolutional neural network was proposed in this study. Methods: The dataset is taken from the PatchCamelyon subset, which contains 220,025 images divided into training, validation, and testing sets at a ratio of 60:20:20. The pretrained ResNet50 model was utilized, and transfer learning was subsequently applied to adjust the weights of the model. To elevate the model performance, the evaluation metrics were assessed by the accuracy score, confusion matrix, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) score. Results: As a result, the proposed algorithm obtained high performance, with scores over 95% in all the evaluation methods, especially the AUC score, which achieved 0.989. Moreover, the model is validated in a testing set with the test-time augmentation (TTA) technique to enhance prediction quality and reduce generalization error. Conclusion: Overall, the proposed model achieves high accuracy when applying transfer learning. The results prove that the trained Resnet50 model can extract useful information from small cells in histopathologic images for breast cancer detection.