Photoacoustic Image Classification and Segmentation of Breast Cancer: A Feasibility Study

Abstract

Nowadays, breast cancer has increasingly threatened the health of human, especially females. However, breast cancer is still hard to detect in the early stage, and the diagnostic procedure can be time-consuming with abundant expertise needed. In this paper, we explored the deep learning algorithms in emerging photoacoustic tomography for breast cancer diagnostics. Specifically, we used a pre-processing algorithm to enhance the quality and uniformity of input breast cancer images and a transfer learning method to achieve better classification performance. Besides, by comparing the area under the curve, sensitivity, and specificity of support vector machine with AlexNet and GoogLeNet, it can be concluded that the combination of deep learning and photoacoustic imaging has the potential to achieve important impact on clinical diagnostics. Finally, according to the breast imaging reporting and data-system levels, we divided breast cancer images into six grades and designed a segmentation software for identifying the six grades of breast cancer. Then, we tested based on MAMMOGRAPHYC IMAGES DATABASE FROM LAPIMO EESC/USP (Laboratory of Analysis and Processing of Medical and Dental Images) to verify the accuracy of our segmentation method, which showed a satisfactory result.