Elevating Medical Image in Healthcare through Deep Learning

Medical imaging is essential to healthcare because it helps with disease diagnosis and treatment planning, and recent advances in deep learning (DL) have greatly increased the accuracy and efficiency of medical image analysis. This study examines how DL models, specifically convolutional neural networks (CNNs) and generative adversarial networks (GANs), can be used to improve the quality and classification of medical images. Using methods like data augmentation, transfer learning, and automated feature extraction, DL models achieve high accuracy in detecting diseases like cancer and heart failure. It also examines the effectiveness of various super-resolution techniques, such as SRCNN, SRGAN, and ESRGAN, for improving medical images. Experimental results show that ESRGAN performs better than other approaches, achieving the highest Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), indicating superior.