An Integrated Computational Framework for Lung Cancer Detection: Combining Deep Learning, Interactive Visualization, and Diagnostic Reporting from CT Imaging

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, necessitating early and accurate diagnostic solutions. This paper presents an integrated computational framework for lung cancer detection from CT imaging, combining deep learning models with interactive visualization and automated diagnostic reporting. The proposed system leverages two state-of-the-art architectures—EfficientNet with CBAM attention enhancement and Vision Transformer including Swin Transformer variants—to achieve robust classification performance. EfficientNet provides parameter-efficient feature extraction through its compound scaling strategy, while Vision Transformers capture global contextual relationships via self-attention mechanisms, addressing the inherent limitations of CNNs in modeling long-range dependencies in medical images. The framework integrates a hybrid feature fusion approach, interactive visualization modules for clinician interpretability, and automated diagnostic report generation. Experimental evaluation on benchmark datasets demonstrates superior performance, with the optimized CBAM-EfficientNet achieving 99.81% accuracy and the ViT-based fusion approach achieving 99.28% accuracy. The system's interactive visualization capabilities, including Grad-CAM attention maps, enhance clinical interpretability and trustworthiness. This research contributes a comprehensive end-to-end solution bridging computational innovation with clinical practice for improved lung cancer diagnosis.