Integrating GANs, Contrastive Learning, and Transformers for Robust Medical Image Analysis

Despite the widespread success of convolutional neural networks (CNNs) in general computer vision tasks, their application to complex medical image analysis faces persistent challenges. These include limited labeled data availability, which restricts model generalization; class imbalance, where minority classes are underrepresented and lead to biased predictions; and inadequate feature representation, since conventional CNNs often struggle to capture subtle patterns and intricate dependencies characteristic of medical imaging. To address these limitations, we propose CTNGAN, a unified framework that integrates generative modeling with Generative Adversarial Networks (GANs), contrastive learning, and Transformer architectures to enhance the robustness and accuracy of medical image analysis. Each component is designed to tackle a specific challenge: the GAN model mitigates data scarcity and imbalance, contrastive learning strengthens feature robustness against domain shifts, and the Transformer captures long-range spatial patterns. This tripartite integration not only overcomes the limitations of conventional CNNs but also achieves superior generalizability, as demonstrated by classification experiments on benchmark medical imaging datasets, with up to 98.5% accuracy and an F1-score of 0.968, outperforming existing methods. The framework's ability to jointly optimize data generation, feature discrimination, and contextual modeling establishes a new paradigm for accurate and reliable medical image diagnosis.