Deep Learning Solution for Medical Imaging Challenges

Abstract

Medica imaging plays a crucial role in diagnosis and treatment planning. However, the large volume and high resolution of images like CT, MRI and X-rays create storage and transmission challenges, especially in telemedicine ans resource-constrained environments. Traditional Compression methods (e.G. JPEG, JPEG2000) often fail to maintain diagnostic quality, prompting the need for advanced approaches. With the rise of artificial intelligence, deep learning-based compression offers promising results in preserving critical images features while significantly reducing file size. Deep learning-based compression techniques, especially CNNs and GANs represent a significant advancement in medical image storage and transmission. Additionally, inference time and computational requirements were analysed to retain diagnostic fidelity, making them highly suitable for telehealth, archiving and mobile diagnostics. Future work can explore hybrid models and integration with PACS systems for clinical deployment.