Adjuvant Diagnosis of Breast Cancer by Deep Learning

The rapid development of deep learning has sparked a lot of interest in its application to medical-image processing issues. Cancer, as one of the most intractable diseases, needs high accurate and efficient adjuvant detection. Our study compared and developed a deep learning algorithm by using CNN models from multiple dimensions that can accurately detect breast cancer. One of our depthwise separable convolutional network methods for classifying breast cancer based on histopathologic scans of lymph node data attain excellent performance compared to the previous investigation to other applications. This method achieves 93.41% in test accuracy, 0.9568 in recall lastly. The remarkable recall performance shows the diagnosis is almost completely correct for benign tumors. However, there is still a small chance of misdiagnosis for malignant tumors so that no patient is spared. Such results are ideal for the adjuvant diagnosis of breast cancer. We also demonstrate that the depth of the network and the computation and number of parameters play critical roles. These findings demonstrate that deep learning approaches can be readily trained to achieve high accuracy and efficiency in breast cancer classification. They hold enormous promise for enhancing clinical diagnosis methods and being more portable.