RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification

Lung cancer remains the leading cause of cancer-related deaths worldwide. Pulmonary nodules, indicative of tumor growth, present significant diagnostic challenges due to their varying sizes and shapes. Computed Tomography (CT) is commonly used for lung cancer screening due to its high sensitivity and efficacy in detecting these nodules. However, differentiating between benign and malignant nodules can be difficult due to their overlapping characteristics. To address this challenge, we propose a Rebuild Convolution Block Attention Module-based Convolutional Neural Network (RCBAM-CNN) designed to accurately classify lung nodules from CT scans. The RCBAM-CNN integrates a Rebuild Convolution Block Attention Module (RCBAM), which includes reshaped layers and redefined spatial attention mechanisms to enhance the network’s focus on relevant features while minimizing noise. The performance of the proposed method is evaluated using the LIDC-IDRI dataset. Data augmentation techniques, including rotation, rescaling, and both vertical and horizontal flips, are applied to improve the model’s robustness and generalization. Subsequently, U-Net is employed for precise image segmentation, ensuring accurate delineation of nodule regions. The proposed RCBAM-CNN demonstrates exceptional performance, achieving an accuracy of 99.72%, surpassing existing methods such as adaptive morphology with a Gabor Filter (GF) and Capsule Network-based CNN. This approach represents a significant advancement in lung nodule classification, offering improved diagnostic accuracy and reliability.