Integrate Deep Learning and Image Processing to Improve Low-Contrast Medical Images: Advancements in Image Enhancement

Abstract

Medical imaging is necessary for the diagnosis, treatment, and monitoring of sickness. Technology, background noise, and patient features can all contribute to low contrast in medical images. Diagnostic errors are caused by poor contrast, which obscures important anatomical structures and disease characteristics. To enhance lowcontrast medical images, the study employed deep learning in conjunction with conventional image processing techniques. Combining CNNs with sophisticated image enhancement techniques such as histogram equalization, contrast stretching, and noise reduction, this system produces high-quality results. Automated contrast enhancement with structural preservation is a capability of deep learning models that learn complicated visual cues. Supervised and unsupervised learning lead to better X-ray, CT, and MRI generalization. By incorporating attention processes, the model is able to better strengthen its focus on diagnostically relevant regions. Images are uniformly and artifact-free post-processed by the system. Improvements have been made to the image’s contrast, clarity, and diagnostic accuracy. PSNR and SSIM improve the quality of the image. Clinical decision-making and patient outcomes can be improved using automated, robust medical image enhancement solutions that deep learning and image processing can provide, as demonstrated in this work.