A Novel CNN-Based Feature Fusion Framework for Breast Cancer Ultrasound Image Classification

Breast cancer remains a major global health concern and is among the leading causes of cancer-related deaths in women. Timely and precise diagnosis significantly improves treatment outcomes and patient survival rates. This paper presents a novel deep learning-based framework for breast cancer classification using ultrasound imagery, built upon the concatenation of two pre-trained Convolutional Neural Network (CNN) models: VGG19 and EfficientNetB0. By leveraging transfer learning and combining heterogeneous feature representations, the proposed method enhances the discriminative power of the extracted features. The model is evaluated on a publicly available benchmark ultrasound dataset and assessed through standard performance indicators, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to generate interpretability heatmaps, visually highlighting regions that contribute most to classification outcomes. The experimental findings reveal that the integrated architecture outperforms several existing approaches as well as individual CNN baselines. This study contributes to the growing field of AI-assisted medical diagnostics and demonstrates the effectiveness of model fusion in ultrasound-based breast cancer detection.