Improving the classification ability of network utilizing fusion technique in contrast-enhanced spectral mammography.

Purpose: Contrast-enhanced spectral mammography (CESM) is an effective tool for diagnosing breast cancer with the benefit of its multiple types of images. However, few models simultaneously utilize this feature in deep learning-based breast cancer classification methods. To combine multiple features of CESM and thus aid physicians in making accurate diagnoses, we propose a hybrid approach by taking advantages of both fusion and classification models.

Methods: We evaluated the proposed method on a CESM dataset obtained from 95 patients between ages ranging from 21 to 74 years, with a total of 760 images. The framework consists of two main parts: a generative adversarial network based image fusion module and a Res2Net-based classification module. The aim of the fusion module is to generate a fused image that combines the characteristics of dual-energy subtracted (DES) and low-energy (LE) images, and the classification module is developed to classify the fused image into benign or malignant.

Results: Based on the experimental results, the fused images contained complementary information of the images of both types (DES and LE), whereas the model for classification achieved accurate classification results. In terms of qualitative indicators, the entropy of the fused images was 2.63, and the classification model achieved an accuracy of 94.784%, precision of 95.016%, recall of 95.912%, specificity of 0.945, F1_score of 0.955, and area under curve of 0.947 on the test dataset, respectively.

Conclusions: We conducted extensive comparative experiments and analyses on our in-house dataset, and demonstrated that our method produces promising results in the fusion of CESM images and is more accurate than the state-of-the-art methods in classification of fused CESM.