Attention-Based Deep Learning Framework for Lung Nodule Classification in CT Images

Lung cancer continues to be one of the leading causes of cancer-related deaths worldwide, as pulmonary nodules are often diagnosed at later stages. Therefore, accurate nodule classification is crucial for enabling early detection and supporting timely clinical decision-making. This study proposes a computer-aided diagnosis (CAD) system for lung nodule classification using computed tomography (CT) images, specifically focused on malignancy prediction and structural morphology analysis. The proposed framework is based on a novel attention-based Convolutional Neural Network (CNN) that incorporates both channel-wise and spatial attention mechanisms. This dual-attention structure enables the model to emphasize diagnostically relevant features while suppressing irrelevant background information, thereby improving interpretability and classification accuracy. For benchmarking purposes, CNN, CNN-SVM, and ResNet101 architectures were implemented for comparison. Experimental results on the LIDC-IDRI dataset for binary classification (benign vs. malignant) and on the IQ-OTH/NCCD dataset for both binary and three-class (normal, benign, malignant) classification tasks demonstrate that the proposed Attention-Based CNN outperforms all baseline models, achieving a maximum classification accuracy of 98% in the binary setting. In addition to accuracy, the proposed model achieves strong performance across multiple evaluation metrics, including precision, recall, F1-score, AUC, and separately reported confusion matrices for both binary and multiclass evaluations, indicating the robustness of the approach. The dual-attention mechanism enhances salient feature localization and discriminative representation learning, thereby contributing to improved performance in both binary and multiclass classification tasks