GAN-Enhanced Deep Learning Framework for High-Precision Lung Nodule Detection and Severity Assessment in CT Images

This study introduces a novel GAN-enhanced deep learning framework for high-precision lung nodule detection and severity assessment in CT images. Our approach addresses critical challenges in medical image analysis by synergizing generative adversarial networks (GANs) for data augmentation with an improved convolutional neural network (ICNN) classifier. The framework incorporates several key innovations: a conditional GAN for generating synthetic, high-fidelity CT images of lung nodules; the Deep Lion Artificial Bee Colony (DLionABC) algorithm for advanced nodule segmentation; a multi-scale feature extraction pipeline capturing deep texture, shape, and intensity characteristics; and an optimized ICNN classifier tailored for lung CT data. Additionally, we develop an automated severity staging system for nodule risk stratification. Rigorous evaluation on both the LIDC-IDRI public dataset and a local clinical dataset of 1,000 CT scans demonstrates significant improvements in accuracy (97.5%), sensitivity (96.8%), specificity (98.1%), and area under the ROC curve (0.991). This research not only advances the technical capabilities of AI-assisted lung cancer detection but also offers insights into the synergistic potential of generative and discriminative deep learning models in medical imaging. The framework’s ability to generate realistic synthetic data while maintaining high classification accuracy addresses critical bottlenecks in current CAD systems, potentially expediting clinical decision-making. Future work will focus on exploring more advanced GAN architectures, investigating model interpretability, and conducting prospective clinical trials to validate the system’s efficacy in real-world diagnostic settings.