Abstract
Transfer learning with convolutional neural networks (CNNs) for breast ultrasound tumor classification can be susceptible to overfitting and unstable generalization when training data are limited, noisy, and heterogeneous. Sharpness-Aware Minimization (SAM) is an optimizer-level training strategy that encourages convergence to flatter regions of the loss landscape and may improve held-out performance without modifying model architecture. In this study, we systematically evaluated SAM for breast ultrasound image classification across seventeen CNN architectures spanning five families–VGG/AlexNet, ResNet, DenseNet, MobileNet, and EfficientNet–using four experiments on two datasets (BUSI and BUS-UCLM). We compared standard Adam versus SAM+Adam and standard stochastic gradient descent (SGD) versus SAM+SGD under matched experimental settings. On BUSI, SAM improved mean validation accuracy by +2.30 percentage points with Adam and by +2.51 percentage points with SGD. On BUS-UCLM, the corresponding mean gains were +2.27 and +2.55 percentage points. The largest individual improvement was observed for MobileNetV3 Small under SAM+SGD (+4.86 percentage points on BUSI), while the highest validation accuracy was achieved by VGG19 with SAM+SGD (96.20% on BUSI). Improvements were also observed in precision, recall, and F1-score, and paired statistical testing across architectures showed consistently positive effects with large effect sizes. Computational analysis showed moderate training-time overhead but no architectural inference overhead, supporting the practical feasibility of SAM. Overall, these findings indicate that SAM is a useful and broadly effective training enhancement for transfer-learned breast ultrasound classification across multiple CNN families and two independent datasets.
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