Prediction and analysis of breast cancer based on transfer learning from DDSM mammography images

Abstract

Breast cancer is a cancer mostly found in women, causing a high morality rate. It appears as a lump in the breast initially, and as cancer cells spread, the other organs are endangered. Research shows that the earlier the breast cancer is detected, the higher rate of recovering will be. The main methods of breast cancer detection are mainly using various physical methods to probe the internal condition of the patients breast. Breast X-ray imaging, also known as mammography, is the most widely used. It uses low-dose X-ray technology to observe the breast tissue and help doctors figure out the exact condition of the patient. With the advancements in deep learning and image recognition, the analysis of mammograms can be previously done by the computer, easing the pressure on doctors. Digital Database for Screening Mammography (DDSM) dataset is a mammography dataset specifically designed for the development of mammography detection models. It contains huge amounts of mammograms and provides the labels corresponding to them. In this study, the performances of normal deep learning model and transfer learning model on the DDSM dataset are compared. Pretrained neural network models are employed and fine-tuned on the DDSM dataset to adapt to the features of mammographic images. Experimental results demonstrate significant improvements through transfer learning, outperforming traditional methods with Convolutional Neural Network (CNN). Finally, the models are evaluated in multiple aspects, conclusions and prospects are analysed based on the results.