ADW-SwinUNet for Skin Melanoma Electrical Impedance Tomography Image Reconstruction Using a Honeycomb-Structured Sensor

Abstract

Melanoma is a highly malignant skin tumor, and early detection with accurate boundary delineation is critical for improving patient survival. Electrical impedance tomography (EIT) is a promising technique for melanoma detection due to its noninvasiveness, low cost, and portability. However, current EIT reconstruction methods are prone to boundary blurring, structural distortion, and missing details owing to the highly ill-posed nature of the inverse problem. To address this issue, this paper develops an Adaptive Dynamic Window SwinUNet (ADW-SwinUNet) for EIT-based melanoma imaging. The acquired 16 × 13 boundary voltage vector is transformed into a 128 × 128 feature map and then fed into the network. By introducing an ADW mechanism, the network enables hierarchical multi-scale feature modeling, thereby improving the balance between global conductivity representation and local boundary recovery. In addition, a 16-electrode honeycomb-structured handheld sensor is designed to improve the sensitivity distribution within the sensing domain. Compared with the conventional ring-shaped configuration, the proposed planar electrode configuration enhances the sensitivity in the central and transition regions, thereby improving the response to internal abnormalities. Simulation results demonstrate that ADW-SwinUNet outperforms Tikhonov regularization, convolutional neural network-based methods, and DHU-Net, a high-capacity network based on an improved U-Net architecture, achieving an average root mean square error (RMSE) of 0.044 and an intraclass correlation coefficient of 0.98. Experimental results validate that the proposed method exhibits high robustness, with RMSE values ranging from 0.06 to 0.10. These results indicate the potential of the proposed method for noninvasive and portable EIT-based skin melanoma imaging.