Multi-classification of breast cancer pathology images based on a two-stage hybrid network.

BACKGROUND AND OBJECTIVE

In current clinical medicine, pathological image diagnosis is the gold standard for cancer diagnosis. After pathologists determine whether breast lesions are malignant or benign, further sub-type classification is often necessary.

METHODS

For this task, this study designed a multi-classification model for breast cancer pathological images based on a two-stage hybrid network. Due to limited sample size for breast sub-type data, this study selected the ResNet34 network as the base network and improved it as the first-level convolutional network, using transfer learning to assist network training. In order to compensate for the lack of long-distance dependencies in the convolutional network, the second-level network was designed to use Long Short-Term Memory (LSTM) to capture contextual information in the images for predictive classification.

RESULTS

For the 8 sub-types of breast cancer classification on the BreakHis (40×, 100×, 200×, 400×) dataset, the ensemble model achieved accuracy rates of 93.67%, 97.08%, 98.01%, and 94.73% respectively. For the 4 sub-types of breast cancer classification on the ICIAR2018 (200×) dataset, the ensemble model achieved accuracy, precision, recall, and F1 Score rates of 93.75%, 92.5%, 92.5%, and 92.5% respectively.

CONCLUSION

The results show that the multi-classification model proposed in this study outperforms other methods in terms of classification performance, and further demonstrate that the proposed RFSAM module is beneficial for improving model performance.