Improved Framework for Identifying Lung Nodules in CT Images Using Deep Learning Techniques
Abstract
This article details a sophisticated deep learning paradigm engineered for the autonomous localization and characterization of pulmonary nodules in Computed Tomography (CT) imaging. Whereas traditional computer-aided diagnosis (CAD) systems are frequently limited by elevated false-positive rates and a failure to integrate global spatial dependencies, the proposed architecture employs a synergistic hybrid approach. Specifically, it leverages Enhanced Convolutional Neural Networks (CNN) for fine-grained local feature extraction in conjunction with Vision Transformers (ViT) to facilitate comprehensive global contextual modeling.
Validated against the LIDC-IDRI and LUNA16 benchmarks, the methodology incorporates rigorous preprocessing protocols, including anisotropic diffusion filtering and the Synthetic Minority Oversampling Technique (SMOTE) to mitigate class imbalance. Empirical evaluations yield a classification accuracy of 98.34%, representing a substantial reduction in diagnostic discrepancies and providing a robust foundation for early-stage clinical intervention.
References
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