Towards Explainable Segmentation of Complex Boundaries in Lung Nodule Detection

Many deep learning models are computationally expensive while capturing complex edges in tasks such as lung nodule segmentation from 2D CT scans. Also, the lack of explainability hinders their adoption for clinical use. To address these challenges, this work proposes a Sobel-enhanced edge-aware powered U-Net architecture capable of emphasising the edges of nodules in lung computed tomography images. The model is trained and evaluated on the benchmark LIDC-IDRI dataset. To provide interpretability, four post hoc explainers were employed: Grad-CAM, Score-CAM, Layer-CAM, and Counterfactual explainability. The proposed model achieved competitive performance across several metrics, including accuracy, dice score, intersection over Union, sensitivity, and specificity, when compared with three baseline models-- U-Net, ResUnet++, and U-Net++. Although it has slightly more parameters (3.4 million) than the U-Net (3.3 million), its ability to identify complex edges of lung nodules makes it stand out. Moreover, the four explainers effectively generated heatmaps that highlight the detected edges. The proposed model delivers competitive segmentation performance with improved edge detection and explainability, highlighting its potential for clinical deployment.