Advances in Deep Learning Techniques for Real-Time Medical Image Processing Applications

Abstract

Recent advancements in deep learning have revolutionized medical image processing by enabling accurate, fast, and automated analysis of complex imaging data. Real-time medical imaging applications such as tumor detection, organ segmentation, and diagnostic support systems require both high accuracy and low latency. This paper presents a comprehensive study of deep learning techniques applied to real-time medical image processing, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models. The study evaluates performance improvements, computational efficiency, and integration with clinical systems. Results indicate that deep learning-based frameworks significantly outperform traditional image processing techniques in both accuracy and adaptability while achieving near real-time performance with optimized architectures.