Convolutional Autoencoder-Based medical image compression using a novel annotated medical X-ray imaging dataset

Abstract

With the ongoing development of machine learning techniques, it is now necessary to train and evaluate these algorithms to have access to high-quality medical X-ray datasets. This study unfolds on two critical axes within the realm of medical imaging. We introduce the proposed Medical X-ray Imaging Dataset (MXID), a meticulously curated resource featuring images spanning 18 body parts. It has been refined to include a classification based on body type within each gender category. This dataset addresses the limitations of existing datasets by offering comprehensive coverage, precise annotations, and optimal image quality. While numerous datasets often fall short in terms of encompassing a broad range of anatomical regions, particularly those that are designed for multi-body component analysis. Our primary contribution lies in bridging the gap in available resources, providing a foundation of developing robust machine learning algorithms in medical imaging. Moreover, we extend our investigation to the integration of the MXID dataset into the domain of medical image compression, evaluating the performance of techniques such as Principal Component Analysis (PCA), K-means clustering, Convolutional Neural Network (CNN), Deep Convolutional Autoencoders (DCAEs), Autoencoders (AEs), and Variational Autoencoder (VAE), using two scenarios, one is for single image compression, and the other is for MXID dataset, which offer a unique perspective that enables a comprehensive assessment of these techniques across diverse anatomical regions. Our study contributes to advancing machine learning and deep learning applications in medical imaging, emphasizing the creation of a dataset and its integration into image compression.