# X-Ray Image Compression Using Convolutional Recurrent Neural Networks

## TL;DR

This paper introduces a novel convolutional recurrent neural network for efficient X-ray image compression, achieving better quality and flexibility than existing methods, which is crucial for healthcare data management and telemedicine.

## Contribution

It presents the first deep convolutional RNN approach for medical image compression, capable of variable compression rates with a single trained model.

## Key findings

- Improved SSIM and PSNR metrics over state-of-the-art techniques.
- Effective variable compression rates with a single trained model.
- Superior performance compared to JPEG 2000.

## Abstract

In the advent of a digital health revolution, vast amounts of clinical data are being generated, stored and processed on a daily basis. This has made the storage and retrieval of large volumes of health-care data, especially, high-resolution medical images, particularly challenging. Effective image compression for medical images thus plays a vital role in today's healthcare information system, particularly in teleradiology. In this work, an X-ray image compression method based on a Convolutional Recurrent Neural Networks RNN-Conv is presented. The proposed architecture can provide variable compression rates during deployment while it requires each network to be trained only once for a specific dimension of X-ray images. The model uses a multi-level pooling scheme that learns contextualized features for effective compression. We perform our image compression experiments on the National Institute of Health (NIH) ChestX-ray8 dataset and compare the performance of the proposed architecture with a state-of-the-art RNN based technique and JPEG 2000. The experimental results depict improved compression performance achieved by the proposed method in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics. To the best of our knowledge, this is the first reported evaluation on using a deep convolutional RNN for medical image compression.