# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
###### Abstract  
Machine Learning (ML), particularly Deep Learning (DL), has become increasingly integral to medical imaging, significantly enhancing diagnostic processes and treatment planning. By leveraging extensive datasets and advanced algorithms, ML models can analyze medical images with exceptional precision. However, their effectiveness depends on large datasets, which require extended training times for accurate predictions. With the rapid increase in data volume due to advancements in medical imaging technology, managing the data has become increasingly challenging. Consequently, irreversible compression of medical images has become essential for efficiently handling the substantial volume of data. Extensive research has established recommended compression ratios tailored to specific anatomies and imaging modalities, and these guidelines have been widely endorsed by government bodies and professional organizations globally. This work investigates the effects of irreversible compression on DL models by reviewing the relevant literature. It is crucial to understand how DL models respond to image compression degradations, particularly those introduced by JPEG and JPEG2000--both of which are the only permissible irreversible compression techniques in the most commonly used medical image format--the Digital Imaging and Communications in Medicine (DICOM) standard. This study provides insights into how DL models react to such degradations, focusing on the loss of high-frequency content and its implications for diagnostic interpretation. The findings suggest that while existing studies offer valuable insights, future research should systematically explore varying compression levels based on modality and anatomy, and consider developing strategies for integrating compressed images into DL model training for medical image analysis. medical image quality; deep learning; machine learning; DICOM image compression; medical images; irreversible compression +
Footnote †: journal: Applied sciences  
and are supported in the DICOM [8] standard, technically allowing the use of acceptable compression ratios for different imaging modalities and anatomical regions in the protocol [9]. Furthermore, irreversible compression for medical images supported by numerous published research findings, case studies, and reviews has received regulatory approval from government bodies and professional organizations worldwide, including as follows:  
* The European Society of Radiology (ESR), which offers guidelines for image compression and data management in Europe, emphasizing the need to maintain high diagnostic quality while managing large volumes of imaging data [6]. * The Canadian Association of Radiologists (CAR), which provides guidelines for image compression and image quality management in Canada. These guidelines include recommended compression ratios based on modality and anatomy [10]. * The Royal Australian and New Zealand College of Radiologists (RANZCR), which recommends the use of lossless compression where possible and lossy compression according to the recommended compression ratios provided by CAR [11]. * The American College of Radiology (ACR), which does not provide specific compression ratios or algorithms; however, it is the responsibility of the qualified physician to ensure diagnostic accuracy and maintain image integrity across various imaging modalities when using irreversible compression [12]. * The Royal College of Radiologists (RCR) in the United Kingdom, which has established guidelines for employing lossy compression in clinical settings. They provide recommendations for compression ratios for primary diagnosis, depending on the imaging modality [13]. * The German Rontgen Society (DRG), which has issued guidelines for the lossy compression of digital radiological images, including recommended compression ratios based on modality [14]. Although there is a consensus on setting some limits for irreversible compression of medical images, a unified standard for acceptable compression ratios has not been established.