An attention aided wavelet convolutional neural network for lung nodule characterization.

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Abstract

Lung cancer is a leading cause of cancer-related mortality worldwide, necessitating the development of accurate and efficient diagnostic methods. Early detection and accurate characterization of pulmonary nodules significantly influence patient prognosis and treatment planning and can improve the five-year survival rate. However, distinguishing benign from malignant nodules using conventional imaging techniques remain a clinical challenge due to subtle structural similarities. Therefore, to address this issue, this study proposes a novel two-pathway wavelet-based deep learning computer-aided diagnosis (CADx) framework forimproved lung nodule classification using high-resolution computed tomography (HRCT) images. The proposed Wavelet-based Lung Cancer Detection Network (WaveLCDNet) is capable of characterizing lung nodules images through a hierarchical feature extraction pipeline consisting of convolutional neural network (CNN) blocks and trainable wavelet blocks for multi-resolution analysis. The introduced wavelet block can capture both spatial and frequency-domain information, preserving fine-grained texture details essential for nodule characterization. Additionally, in this work, convolutional block attention module (CBAM) based attention mechanism has been introduced to enhance discriminative feature learning. The extracted features from both pathways are adaptively fused and processed using global average pooling (GAP) operation. The introduced WaveLCDNet is trained and evaluated on the publicly accessible LIDC-IDRI dataset and achieved sensitivity, specificity, accuracy of 96.89%, 95.52%, and 96.70% for nodule characterization. In addition, the developed framework was externally validated on the Kaggle DSB2017 test dataset, achieving 95.90% accuracy with a Brier Score of 0.0215 for lung nodule characterization, reinforcing its reliability across independent imaging sources and its practical value for integration into real-world diagnostic workflows. By effectively combining multi-scale convolutional filtering with wavelet-based multi-resolution analysisand attention mechanisms, the introduced framework outperforms different recent most state-of-the-art deep learning models and offers a promising CADx solution forenhancing lung cancer screening early diagnosis in clinical settings.

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