A Novel F-Net Model for Robust Breast Tumor Segmentation Via Transfer Learning

Breast cancer remains one of the most prevalent and life-threatening diseases among women worldwide. Accurate and early segmentation of tumors in ultrasound images plays a critical role in reducing mortality and guiding treatment. This paper presents a novel deep learning framework that combines an Attention U-Net with a Transformer architecture to enhance segmentation performance. The proposed fused model effectively captures both local spatial features and long-range dependencies, improving delineation of tumor boundaries. In contrast to conventional training paradigms, we propose a sequential learning strategy: the model is first trained on benign tumor images and subsequently fine-tuned on malignant cases. Experimental evaluations on the BUSI dataset demonstrate that our approach significantly outperforms traditional UNet, TransU-Net, and standalone Attention U-Net models. Notably, the fused model achieves superior performance in segmenting malignant tumors, validating the effectiveness of combining attention mechanisms with global contextual learning through the proposed incremental transfer learning approach.