This study seeks to enhance the classification performance of breast ultrasound images, addressing the challenges of difficult and costly collection of breast ultrasound datasets as well as the discrepancies in feature distribution of the collected datasets. Performance is enhanced by using a mix of generative adversarial networks (GAN) and domain adaptive networks. First, an adaptive layer is first added to the basic model of the gradually vanishing bridge (GVB), to better match the feature distributions of the source and target domains of the dataset. The multi-kernel maximum mean discrepancy (MK-MMD), which is the most efficient of existing adaptive approaches, is implemented in the fully connected layer of the original model’s feature extraction network. Finally, through the process of fine-tuning, the model that has the highest level of overall performance is determined. In experiments, the proposed method surpassed the conventional unsupervised domain adaptation (DDC) and adversarial domain adaptation (MK_DAAN, GVB) in performance, achieving 85.11% accuracy, 97.48% recall, and 0.92 F1-score.

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