Explainable and Uncertainty-Aware Deep Learning for CT-Based Pulmonary Nodule Analysis – A Preview

Computer Tomography (CT) analysis of Chest images facilitates early detection and malignancy stratification of pulmonary nodules. Currently employed Computer-Aided Diagnosis systems (CAD) still have issues with assorted image quality, inter-observer inconsistency, inadequate interpretability, recognition of false positives and insufficient uncertainty assessment. This paper proposes an explainable and uncertainty-aware deep learning architecture for CT-based pulmonary nodule analysis. The framework combines adaptive pre-processing using swarm optimization, transformer-assisted U-Net segmentation, hybrid convolutional neural network (CNN) and Vision Transformer malignancy assessment, and an explainability layer employing SHAP, Grad-CAM, and LIME. Prediction reliability is communicated through a confidence-aware uncertainty layer. LIDC-IDRI and LUNA16 are identified as benchmark datasets. Existing works focus on individual stage optimization rather than integrating uncertainty, explainability, cross-dataset resilience, and false-positive control onto a unified framework. The proposed work proposes a unified research path for interpretable and confidence-aware pulmonary nodule analysis from CT images supporting computer-aided screening and research assessment, with future validation required before any potential clinical deployment.