UltraSegNet: A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise, dependency on the operator, and the variation of image quality. This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations: This work adds three things: (1) a changed ResNet-50 backbone with sequential 3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries; (2) a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory; and (3) an adaptive feature fusion strategy that changes local and global features based on how the image is being used. Extensive evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance: On the BUSI dataset, it obtains a precision of 0.915, a recall of 0.908, and an F1 score of 0.911. In the UDAIT dataset, it achieves robust performance across the board, with a precision of 0.901 and recall of 0.894. Importantly, these improvements are achieved at clinically feasible computation times, taking 235 ms per image on standard GPU hardware. Notably, UltraSegNet does amazingly well on difficult small lesions (less than 10 mm), achieving a detection accuracy of 0.891. This is a huge improvement over traditional methods that have a hard time with small-scale features, as standard models can only achieve 0.63–0.71 accuracy. This improvement in small lesion detection is particularly crucial for early-stage breast cancer identification. Results from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.