# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
###### Abstract
Table 1 provides a summary of compression ratio recommendations by anatomy and modality as published by CAR, DRG, and RCR [6]. However, it is critical to emphasize that the guidelines also suggest that the final decision regarding the use of irreversible compression, its parameters, and its impact on diagnostic quality should be made at the discretion of a qualified physician who can assess the diagnostic quality of a compressed image. Thus, while the recommended compression ratios provide a general guideline for preserving diagnostic image quality, radiologists must evaluate each case individually. They need to consider specific medical contexts, imaging modalities, characteristics of the pathology and anatomy, and clinical requirements to ensure that the image quality remains adequate for accurate diagnosis. The scientific evidence supporting the use of irreversible compression not only aids in managing long-term storage requirements and the transmission of images but also ensures that the quality of patient care is not compromised. Radiologists and healthcare hardware and software providers around the world rely on these standards to maintain a balance between efficient data storage and the need for high-quality diagnostic images. Irreversible compression reduces file size by eliminating high-frequency content from images. This high-frequency content includes fine details and subtle variations that may not be immediately visible to the human eye but can significantly affect image quality. While the reduction in file size helps decrease storage and transmission costs, it irreversibly leads to some loss of detail, which could potentially impact the diagnostic fidelity of the images in certain cases. While the diagnostic quality of compressed medical images has been extensively studied and documented in the scientific literature, the effects of irreversible compression on the training and performance of ML models remain underexplored. This is particularly significant, as understanding these effects is crucial for developing reliable ML-based tools for medical image analysis. High-frequency content, which is often removed by irreversible compression, could be significant for ML models as it includes fine details that might enhance model performance. Conversely, this high-frequency content can also correspond to noise, and its removal might result in better quality data that represent only the relevant image information. This work reviews the scientific literature to investigate whether irreversible compression of medical images affects the accuracy of ML models. The study aims to determine how ML models respond to image compression degradations. It addresses this problem by presenting scientific knowledge on the degradations caused by JPEG and JPEG2000 compression methods and examining how ML models react to these degradations in the context of medical image analysis. The study aims to provide a comprehensive analysis by:  
* Analyzing JPEG and JPEG2000 compression impacts on medical image quality: focusing on the specific degradations introduced by these compression methods, such as the loss of high-frequency content, and exploring their potential implications for diagnostic human interpretation. * Investigating the performance of DL models on compressed medical images by reviewing the existing literature on the evaluation of various ML models using both compressed and uncompressed medical images. * Providing insights into how these models respond to degradations caused by irreversible compression, including whether the loss of high-frequency content impacts their ability to accurately identify and diagnose medical conditions.