Quantum-enhanced deep learning for elastographic image-based characterization of cutaneous and subcutaneous masses.

Objective: Improving the diagnostic precision of skin and subcutaneous lesion assessments is the main goal of this study. Although elastography is an effective method for determining tissue stiffness, the high-dimensional, non-linear data these scans generate frequently present challenges for traditional deep learning models. To capture fine-grained stiffness patterns that conventional models may overlook, this study proposes and evaluates a hybrid quantum-classical deep learning architecture. Methods: A retrospective dataset of 520 elastographic images with histological confirmation is used in the investigation. The proposed methodology uses a multi-stage hybrid pipeline, in which initial feature vectors are extracted from raw images using a traditional Convolutional Neural Network (CNN). A parameterized quantum circuit layer receives these vectors. This layer recognizes complex textural patterns by performing extensive transformations within a high-dimensional Hilbert space. A final fully connected layer processes the output to categorize lesions as either benign or malignant. We present a proof-of-concept simulation of combining a parameterized quantum layer with a classical CNN backbone. Although the current classical software emulation introduces a latency trade-off (124.5 ms vs. 8.4 ms per image), our result can serve as a baseline for analyzing low-parameter feature representations before running on physical QPU hardware. Results: Compared to fully classical baselines, the hybrid model showed a notable performance improvement. Important conclusions include 94.3% diagnostic accuracy, 93.8% sensitivity, and 94.7% specificity. From 0.921 (classical) to 0.963 (hybrid), the Area Under the ROC Curve (AUC) rose by 4.2%. Testing revealed that, especially in situations with unclear stiffness patterns, the quantum layer was the main source of increased predictive power. Discussion: Quantum circuit integration offers more "expressive power" than conventional neural networks. The model efficiently captures non-linear correlations in tissue density that are typically flattened or ignored by classical architectures, as it operates in a high-dimensional Hilbert space. This implies that combining quantum and classical techniques is especially well-suited to medical imaging, where minute changes in data have a clinically significant impact. Conclusion: The study concludes that when it comes to describing cutaneous and subcutaneous masses, the hybrid classical-quantum model performs noticeably better than conventional deep learning techniques. These results indicate a promising advancement in medical AI that could provide physicians with more accurate and reliable tools for the early identification of cancer using elastographic data.