Generative AI in Medical Imaging: Enhancing Lung Nodule Detection using GANs

Lung cancer continues to be a predominant cause of cancer-related mortality globally, and early diagnosis with computed tomography (CT) imaging markedly enhances patient outcomes. The limited availability of annotated medical imaging data presents a significant obstacle in developing effective deep learning models for lung nodule detection. This paper presents a system based on Generative Adversarial Networks (GANs) to generate realistic lung nodule images, tackling the issues of data imbalance and augmentation in medical imaging. Utilizing the LIDC-IDRI dataset, comprising expert-annotated thoracic CT scans, we train a conditional GAN to produce high-quality synthetic lung nodule patches based on size and malignancy characteristics. Synthetic data is combined with real samples to train a convolutional neural network (CNN) classifier, leading to enhanced performance metrics in accuracy, sensitivity, and AUC-ROC. Qualitative and quantitative assessments indicate that our GAN-generated images closely mimic authentic nodules, as validated by radiomic feature distributions and visual Turing tests. The suggested generative methodology improves model generalizability, facilitates clinical decision-making, and establishes a foundation for data-efficient AI systems in pulmonary diagnostics. This study emphasizes the capacity of generative AI to enhance medical datasets and facilitate early-stage lung cancer identification.