Cloud-enabled Internet of Things Medical Image Processing Compressed Sensing Reconstruction

Abstract

Deep learning compresses medical image processing in IoMT. CS-MRI acquires quickly. It has various medicinal uses due to its advantages. This lowers motion artifacts and contrast washout. Reduces patient pressure and scanning costs. CS-MRI avoids the Nyquist-Shannon sampling barrier. Parallel imagingbased fast MRI uses many coils to reconstruct MRI images with less raw data. Parallel imaging enables rapid MRI. This research developed a deep learning-based method for reconstructing CS-MRI images that bridges the gap between typical non-learning algorithms that employ data from a single image and enormous training datasets. Conventional approaches only reconstruct CS-MRI data from one picture. Reconstructing CS-MRI images. CS-GAN is recommended for CS-MRI reconstruction. For success. Refinement learning stabilizes our C-GAN-based generator, which eliminates aliasing artifacts. This improved newly produced data. Product quality increased. Adversarial and information loss recreated the picture. We should protect the image’s texture and edges. Picture and frequency domain data establish consistency. We want frequency and picture domain information to match. It offers visual domain data. Traditional CS-MRI reconstruction and deep learning were used in our broad comparison research. C-GAN enhances reconstruction while conserving perceptual visual information. MRI image reconstruction takes 5 milliseconds, allowing real-time analysis.