International Journal of BIM and Engineering Science IJBES 2571-1075 10.54216/IJBES https://www.americaspg.com/journals/show/3325 2021 2021 Assistant Professor, Faculty of Engineering, CIST, Chinmaya Vishwa Vidyapeeth, Onakkur, Ernakulam District, Kerala, India K. K. Professor, Dept of ECE, Meenakshi Sundararajan Engineering College, Kodambakkam, Chennai 600024, India A. Babiyola Associate Professor, Department of Electrical and Electronics Engineering, KIT Kalaignarkarunanidhi Institute of Technology, Coimbatore, India K. Dhineshkumar 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. 2024 2024 01 09 10.54216/IJBES.090201 https://www.americaspg.com/articleinfo/22/show/3325