Dynamic-Attentive Pooling Networks: A Hybrid Lightweight Deep Model for Lung Cancer Classification.
Authors
Affiliations (4)
Affiliations (4)
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
- Department of Information Systems and Operations Management, Vienna University of Economics and Business, 1020 Vienna, Austria.
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Abstract
Lung cancer is one of the leading causes of cancer-related mortality worldwide. The diagnosis of this disease remains a challenge due to the subtle and ambiguous nature of early-stage symptoms and imaging findings. Deep learning approaches, specifically Convolutional Neural Networks (CNNs), have significantly advanced medical image analysis. However, conventional architectures such as ResNet50 that rely on first-order pooling often fall short. This study aims to overcome the limitations of CNNs in lung cancer classification by proposing a novel and dynamic model named LungSE-SOP. The model is based on Second-Order Pooling (SOP) and Squeeze-and-Excitation Networks (SENet) within a ResNet50 backbone to improve feature representation and class separation. A novel Dynamic Feature Enhancement (DFE) module is also introduced, which dynamically adjusts the flow of information through SOP and SENet blocks based on learned importance scores. The model was trained using a publicly available IQ-OTH/NCCD lung cancer dataset. The performance of the model was assessed using various metrics, including the accuracy, precision, recall, F1-score, ROC curves, and confidence intervals. For multiclass tumor classification, our model achieved 98.6% accuracy for benign, 98.7% for malignant, and 99.9% for normal cases. Corresponding F1-scores were 99.2%, 99.8%, and 99.9%, respectively, reflecting the model's high precision and recall across all tumor types and its strong potential for clinical deployment.