Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics

The early detection of lung diseases, including cancer, is essential for improving patient outcomes. However, traditional diagnostic approaches and standard deep learning models often face challenges in effectively analyzing the complex spatial and temporal variations in medical imaging data, particularly in CT scans. To address these challenges, we propose DC-CAD, a novel hybrid framework that integrates Dilated Capsule Networks, Channel-wise Attention Mechanisms, and Distanced Long Short-Term Memory for precise and early diagnosis of lung diseases. DC-CAD is innovative in its ability to combine multi-scale feature extraction and temporal dynamic analysis, enabling the model to capture intricate spatial relationships and sequential changes in lung tissue. The model consists of three main contributions: (1) Dilated Capsule Networks for improved multi-scale context aggregation, which captures subtle textural variations, (2) a Channel-wise Attention Mechanism to focus on the most relevant regions of interest, minimizing the impact of irrelevant features, and (3) Distanced LSTM layers to model temporal dependencies across sequential CT scans, providing insights into disease progression. Through comprehensive experiments on the LC25000 dataset, DC-CAD achieves 99.52% accuracy, significantly outperforming baseline models such as standard Capsule Networks and Convolutional Neural Networks. The model also reduces the error rate to 0.48%, demonstrating substantial improvements in diagnostic performance, including increased accuracy, sensitivity, and specificity. These results establish DC-CAD as a powerful and reliable tool for automated lung disease diagnosis, with significant potential to enhance clinical workflow by reducing radiologists’ workload through its interpretability and efficiency. Moving forward, we plan to extend the model to handle multi-modal data and investigate advanced attention mechanisms to further improve diagnostic accuracy and generalizability.