International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Elevating Medical Image in Healthcare through Deep Learning

Author(s) Ms. Himali Patel, Prof. Appurva Kapil
Country India
Abstract 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.
Keywords Deep Learning (DL), Medical Imaging, Convolutional Neural Networks (CNN), Generative Adversarial Networks (GANs), Data Augmentation, Super-Resolution, SRCNN, SRGAN, ESRGAN, Feature Extraction, Image Classification, Disease Diagnosis, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM).
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-07
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.40377
Short DOI https://doi.org/g9dnbv
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