Attention-guided deep neural network with a multichannel architecture for lung nodule classification

Abstract

Detecting and accurately identifying malignant lung nodules in chest CT scans in a timely manner is crucial for effective lung cancer treatment. This study introduces a deep learning model featuring a multi-channel attention mechanism, specifically designed for the precise diagnosis of malignant lung nodules. To start, we standardized the voxel size of CT images and generated three RGB images of varying scales for each lung nodule, viewed from three different angles. Subsequently, we applied three attention submodels to extract class-specific characteristics from these RGB images. Finally, the nodule features were consolidated in the model's final layer to make the ultimate predictions. Through the utilization of an attention mechanism, we could dynamically pinpoint the exact location of lung nodules in the images without the need for prior segmentation. This proposed approach enhances the accuracy and efficiency of lung nodule classification. We evaluated and tested our model using a dataset of 1018 CT scans sourced from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The experimental results demonstrate that our model achieved a lung nodule classification accuracy of 90.11 %, with an area under the receiver operator curve (AUC) score of 95.66 %. Impressively, our method achieved this high level of performance while utilizing only 29.09 % of the time needed by the mainstream model.