Mitosis Detection in Breast Cancer with Deep Learning: A New Approach

In this work, we propose a new approach for spotting mitoses in breast cancer histology images. This new approach involves integrating two publicly accessible datasets following a normalization procedure of color. The mitotic samples are then enhanced by preserving the context to address class imbalance. After this, the candidate mitotic cells are classified into the target classes using a ResNet classifier. Through this method, we were able to accurately identify mitosis in the combined dataset while attempting to identify it in the images. We demonstrate that our method outperforms all current methods by comparing it to state-of-the-art methods using a public dataset. Our results indicate that the proposed technique can be used to automatically identify mitotic cells in the images of histopathology of breast cancer.