BreastSegX: A Pipeline for High-Fidelity Breast Ultrasound Lesion Segmentation

The main contributions of this work
are summarized as follows:
• We pre-trained EfficientNet-B4 [6] on 3,369 external
breast ultrasound images to improve feature representa-
tion before fine-tuning on the BUSI dataset.
• The model was trained using 5-fold cross-validation with
class balancing and data augmentation, then evaluated on
a held-out 10% test set (78 images).
• For post-processing, we applied morphological closing
and connected-component analysis to reduce noise and
smooth tumor boundaries.
• Finally, we proposed specific evaluation metrics for “Nor-
mal” scans to avoid division-by-zero errors and to report
all classes consistently.