Microwave Metasurface-Based Sensor with Artificial Intelligence for Early Breast Tumor Detection

In this paper, a microwave metasurface sensor integrated with artificial intelligence (AI) for breast tumor detection is presented. The sensor's sensitivity is estimated by analyzing shifts in magnitude and the phase of the reflection coefficient (S11) obtained from normal and abnormal breast phantoms. The (S11) responses of 137 anatomically realistic 3D numerical breast phantoms in standard classes, C1-mostly fatty, C2-scattered fibroglandular, C3-heterogeneously dense, and C4-extremely dense, incorporating different tumor sizes are used as input features. A custom neural network is developed to detect tumor presence using the recorded (S11) responses. The model is trained with cross-entropy loss and the AdamW optimizer. The dataset is split into training (70%), validation (15%), and test (15%) sets. The model achieves 99% accuracy, with perfect precision, recall, and F1-score across individual classes. For paired class combinations, accuracies of 71% (C1 with C2) and 65% (C2 with C3) are obtained, while performance degrades to approximately 50% when all four classes are combined. The sensor is fabricated and experimentally validated using two physical breast phantoms, demonstrating reliable detection of a 10 mm tumor. These results highlight the effectiveness of combining microwave metasurface sensing and AI for breast tumor detection.