2571-1075ISSN (Online)
Volume 9 , Issue 2 , PP: 01-09, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
K. R. N. Aswini 1 * , A. Babiyola 2 , K. Dhineshkumar 3 *
Doi: https://doi.org/10.54216/IJBES.090201
Medical imaging has become a critical tool in diagnostics, but low-resolution images often limit the precision of diagnosis and treatment. This study presents a deep learning-based image super-resolution framework designed to enhance the quality and clarity of medical images, specifically tailored for radiology, dermatology, and histopathology. The proposed framework uses a Convolutional Neural Network (CNN) architecture with a Residual Dense Network (RDN) backbone, improving visual details and retaining clinically relevant features. Training on a diverse dataset of MRI, CT, and X-ray images, the model achieved a 35% improvement in Peak Signal-to-Noise Ratio (PSNR) and a 42% improvement in Structural Similarity Index Measure (SSIM) compared to conventional interpolation techniques. Our method also demonstrated an increase of 48% in diagnostic accuracy when integrated into radiological workflows, enhancing radiologists' ability to identify pathologies with subtle visual indicators. Experimental results show that our super-resolution framework provides a fourfold increase in resolution while minimizing computational cost by 30% using optimized GPU-based processing. This innovative approach to super-resolution has the potential to significantly impact the diagnostic field by enabling clearer and more detailed medical imaging for improved patient outcomes.
Deep Learning , Image Super-Resolution , Medical Imaging , Convolutional Neural Networks (CNN) , Residual Dense Network (RDN) , Peak Signal-to-Noise Ratio (PSNR) , Structural Similarity Index Measure (SSIM) , Diagnostic Accuracy
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