A Classification Method of Breast Cancer Histopathological Images Based on Multi-Staged Transfer Learning

Accurate and efficient diagnosis of breast cancer is an important research topic of computer-aided diagnosis. However, due to the lack of breast cancer histopathological image data and the fact that the histopathological images themselves have problems such as cell overlap and uneven color distribution, accurate classification and efficient feature extraction of the breast cancer histopathological images remains still a challenge. To tackle this challenge, this paper proposes a multi-stage transfer learning method (MT) which conducts two sequential transfer learning stages using the general images and the breast cancer medical images, respectively, and then the low-level features of the general images and the high-level features of the breast cancer medical images are fused in the final stage to improve the classification performance. The method was tested on the BreakHis dataset with the images divided into four subclasses (for the benign and the malignant separately) and eight subclasses (the benign and the malignant all together). A comparison of the MT method with some other state-of-the-art methods showed that for the 4 benign subclasses, the MT method achieved accuracy of 99.20%, 98.68%, 96.01%, and 98.24% in 40×, 100×, 200×, and 400× magnification factors, respectively. For the 4 malignant subclasses, the MT method achieved accuracy of 99.37%, 99.12%, 98.74%, and 98.14% in 40×, 100×, 200×, and 400× magnification factors, respectively. Finally, For all the eight subclasses, the MT method achieved accuracy of 97.49%, 96.04%, 96.21%, and 95.05% in 40×, 100×, 200×, and 400× magnification factors, respectively. The experimental results demonstrated the MT method can provide an effective means in the breast cancer classification.