Hierlungxai: A Hierarchical and Explainable Deep Learning Framework for Ct-Based Lung Cancer Classification

Abstract

Accurate detection of lung cancer (LC) from Computed Tomography (CT) scans plays a crucial role in early diagnosis and effective treatment planning. Despite significant progress in deep learning (DL) based classification, current approaches continue to face challenges such as inconsistent intensity normalization, variable slice resolution, noise artifacts, limited dataset availability, subtle nodule appearance, and complex inter-slice dependencies. To address the identified limitations, this study suggests a new approach HierLungXAI, a comprehensive framework integrating advanced preprocessing, hierarchical feature extraction, and explainable AI techniques. The Advanced Image Standardization and Enhancement Pipeline (AISEP) standardizes intensities, enhances contrast, reduces noise, preserves critical structural details, and augments the dataset to improve model generalization. For feature extraction, the HierEffNet (Hierarchical Efficient-based Network) combines deep hierarchical convolutional modules for local nodule-specific details with a hierarchical attention mechanism to capture global volumetric context across consecutive slices. Extracted features are classified using a Multi-Layer Perceptron (MLP), while Grad-CAM provides visual explanations, highlighting key regions influencing predictions. The proposed framework was evaluated on two benchmark datasets, achieving an accuracy of 99.78% and 99.94%, demonstrating superior performance over existing methods. The proposed integration of AISEP preprocessing, HierEffNet hierarchical feature extraction, MLP classification, and Grad-CAM-based interpretability represents a novel approach that simultaneously enhances sensitivity to small and subtle nodules while effectively modeling inter-slice and global contextual relationships, establishing a robust and transparent framework for clinically reliable LC detection.