Machine Learning-based Medical Image Compression Using Principal Component Analysis (PCA) and Autoencoders

Abstract

Medical imaging creates large datasets crucial for diagnosis, posing storage and transmission challenges in digital health systems. Traditional compression methods like JPEG and PNG often lose critical diagnostic details, necessitating more advanced techniques. This study explores machine learning-based compression using Principal Component Analysis (PCA) and Autoencoders to achieve high compression ratios while preserving diagnostic quality. The models were trained and evaluated on medical imaging datasets, comparing reconstruction error, compression ratio, and diagnostic accuracy retention. Results demonstrate that Autoencoders outperform PCA in preserving diagnostically relevant features at higher compression rates.