GAN-based Image Processing Mechanism for High-Quality and Object Recognition Enhancement

This paper presents a study on utilizing Generative Adversarial Networks (GANs) to enhance medical image processing and object recognition. The proposed methods focus on generating high-quality medical images through super-resolution and improving object detection and classification by integrating GAN with YOLO. Additionally, to address challenges in GAN training, such as mode collapse and overfitting, optimization techniques like Spectral Normalization and Gradient Penalty are applied to ensure training stability. By integrating these techniques into the “openEMR” system, the paper demonstrates the practical applicability of GANs in medical image analysis, contributing to the development of more efficient and accurate solutions within EMR systems.