Volumetric ResNet for Enhanced Lung Nodule Classification: A High-Accuracy Approach using Deep Learning

Lung cancer remains a leading cause of mortality, affecting both men and women, has posed significant challenges for accurate identification for decades. Recent advancements in Deep Learning (DL) have revolutionized the field of Computer-Aided Diagnosis (CAD) for detection and classification of lung cancer. This study introduces a novel approach leveraging Volumetric ResNet architecture for accurate classification of candidate Lung Nodules into Nodule and Non-Nodule categories. The pipeline begins with histogram equalization for enhanced Computed Tomography (CT) scan contrast, followed by Volumetric ResNet for classification with false positive reduction. Evaluation on the LUNA16 dataset demonstrates the superiority of the proposed model in terms of accuracy (99.49%), specificity (99.74%), sensitivity (100%), precision (99.75%) with augmentation and of accuracy (99.26%), specificity (98.89%), sensitivity (99.63%), precision (98.90%), False Positive Rate (FPR) 1.11% and False Negative Rate (FNR) 0.37% without augmentation, it surpasses the existing state-of-the-art methods.