Attention-Enhanced Deep Learning Frameworks for Accurate Lung Cancer Detection and Classification
Abstract
Lung cancer is a life-threatening illness and early and proper diagnosis is essential to successful treatment and higher survival rates. Conventional deep learning systems, such as standard YOLO architectures, do not tend to differentiate between visually similar categories like benign and malignant nodules because of constraints in the representation of multi-scale features and understanding of context. To solve these two new modules are proposed in this work, the C3K2 and A2C2F modules in the YOLOv12x framework. C3K2 module enhances multi-kernel convolutional learning with both fine-grained and global spatial features that enhance the ability of the model to distinguish subtle structural differences in lung nodules. At the same time, A2C2F module (Adaptive Attention Cross-Channel Fusion) adapts the channel-wise features representations and improves the information flow between the layers. The experimental assessment of a benchmark lung CT images dataset shows that the proposed YOLOv12x model has better performance compared to the current YOLOv8x architecture. The C3K2 and A2C2F modules are important to the point that they increase feature discrimination, robustness and detection accuracy without decreasing computational efficiency.