Optimizing Medical Imaging with AI: Advanced Cnn and Gan Techniques for Enhanced Diagnostic Efficiency

In recent years, the explosive growth of medical imaging data and the rapid development of artificial intelligence (AI) have transformed the landscape of diagnostic medicine. This paper investigates the optimization of deep learning algorithms—specifically Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN)—to enhance medical image processing. CNNs have proven exceptionally effective in tasks such as image segmentation, lesion detection, and classification by automatically extracting high-level features from raw data. Optimization strategies including parallel computing, quantization, and model pruning have been implemented to accelerate inference speed and reduce energy consumption, making CNNs highly suitable for real-time clinical applications. In contrast, GANs offer unique advantages in generating high-quality synthetic images through adversarial training, thereby addressing issues related to low-resolution imaging and limited data availability. Techniques such as hybrid computing and memory distribution optimization have been explored to boost GAN training efficiency and improve output quality. Despite these advancements, significant challenges remain, including high computational complexity, data privacy concerns, algorithm adaptability, and the necessity for coordinated hardware–software optimization. This paper provides a systematic analysis of the acceleration methods for both CNN and GAN models on AI chips, detailing their strengths, limitations, and practical applications in medical imaging.