Volume 9 • Issue 2 • PP: 01-09 • 2024
Deep Learning-Based Image Super-Resolution for Enhanced Medical Diagnostics
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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.
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