U-Grad: A Grad-CAM-Guided Reduced U-Net for Efficient Lung Cancer Segmentation

Lung cancer is the most common cause of cancer-related death worldwide. The detection of lung nodules from Computed Tomography (CT) scans is essential for assessing disease progression, monitoring treatment response, and guiding therapeutic strategies. Deep learning has emerged as a powerful tool for image segmentation, demonstrating significant potential in medical imaging applications. This work aims to introduce U-Grad, a novel model designed for lung nodule segmentation from 2D CT slices. It integrates an encoder that generates heatmaps using Gradient-weighted Class Activation Mapping (Grad-CAM), which are then concatenated with CT slices and fed into a Reduced U-Net to enhance nodule representation. The Reduced U-Net is characterized by an encoder-decoder structure whose maximum depth, in terms of filter size, is (256,256), Additionally, it employs the Leaky Rectified Linear Unit as an alternative activation function, enhancing its representational capacity. NSCLC Radiogenomics dataset from The Cancer Imaging Archive was used to train and test the proposed U-Grad for 100 epochs. The performance of both the Reduced U-Net and U-Grad models was evaluated using the Dice Coefficient (DC) and the Intersection over Union (IoU) metrics. The results demonstrate that both models outperform existing models in the literature. The Reduced U-Net achieves a DC of 93.15% and an IoU of 89.02%, while U-Grad achieves a DC of 91.27% and an IoU of 86.26% in test set. Although both models exhibit comparable performance, U-Grad demonstrates slightly lower overfitting, making it a more robust alternative. Moreover, U-Grad's ability to generate interpretable heatmaps enhances its utility for clinical applications and research, particularly in resource-limited settings where annotated data are scarce.Clinical relevance- U-Grad is an innovative and effective lung nodule segmentation model that leverages explainable AI techniques to enhance its performance, interpretability and generalizability.