Making use of manufacturing process variations: Machine learning approaches for efficient medical and biological study-based image compression and lossless transmission

This research paper explores advanced machine learning techniques for compressing and transmitting medical and biological study-based images without loss of critical diagnostic information. We investigate deep learning architectures including autoencoders, generative adversarial networks (GANs) and transformer models optimized for various medical and biological imaging modalities. Our proposed hybrid compression pipeline combines semantic segmentation, region-adaptive encoding, and learned post-processing to achieve state-of-the-art compression ratios while preserving clinically relevant features. Extensive experiments on large-scale datasets of X-rays, CT scans, MRI scans and microscopy images demonstrate the efficacy of our approach in terms of compression performance, reconstruction quality, and computational efficiency. We also present a novel blockchain-based system for secure and lossless transmission of the compressed medical and biological data. Our findings indicate that machine learning-driven compression can enable more efficient storage and sharing of medical and biological images in resource-constrained healthcare and research environments.