Capsule Network-Based Framework for Lung Cancer Classification from CT Images

Abstract

Lung cancer remains one of the most critical global health challenges due to its high mortality rate and the complexity of early diagnosis. Computed Tomography (CT) imaging has become an essential modality for identifying pulmonary abnormalities; however, accurate classification of benign and malignant lung lesions remains challenging because of variations in nodule size, shape, texture, and anatomical location. Traditional computer-aided diagnosis approaches have demonstrated effectiveness but often struggle with maintaining spatial relationships between extracted features. This research presents a Capsule Network-Based Framework for Lung Cancer Classification from CT Images, integrating capsule representation learning and dynamic routing mechanisms to improve discriminative feature modeling. The proposed framework focuses on preserving hierarchical spatial information while distinguishing malignant characteristics from benign patterns. The methodology combines CT image preprocessing, lung region segmentation, feature encoding through capsule layers, and dynamic routing-based feature aggregation for classification. Existing studies on deep learning-based lung nodule analysis demonstrate the increasing importance of automated diagnostic systems, while limitations in conventional convolutional approaches motivate the exploration of capsule-based architectures (Gu et al., 2021; Shrestha and Mahmood, 2019). The framework provides a theoretically grounded approach for enhancing medical image interpretation by improving feature robustness and reducing dependence on manual analysis. The research contributes a structured deep learning model for intelligent lung cancer assessment and highlights opportunities for future clinical decision-support applications.