Enhancing Medical Image Quality Using Deep Learning Techniques

With the advent of advanced imaging technologies and the growth of digitization, the use of medical images for the precise diagnosis of patients’ health conditions is increasing daily. However, medical images generated using traditional X-ray machines produce blurred or low-resolution images owing to poor lighting or machines that require maintenance. Medical practitioners face challenges while diagnosing diseases/injuries in certain body parts due to such low-resolution images. This can lead to flawed diagnoses and misleading treatment. To address these challenges, this study uses state-of-the-art deep learning techniques to convert low-resolution images to super-resolution (SR) images. This study focuses on the training and evaluation of two deep learning models, superresolution convolutional neural network (SRCNN) and very deep super-resolution (VDSR), on a benchmark dataset of low-resolution X-ray images. Through rigorous experimentation, the hyperparameters pertaining to these two models were carefully optimized to achieve maximum performance. The performance of both models was assessed using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The experimental results show that the VDSR model outperformed SRCNN, achieving a PSNR of 98.3302 and SSIM of 1.0000, compared with SRCNN's PSNR of 43.5399 and SSIM of 0.9964. The higher value of PSNR in VDSR indicates the model's capability to effectively reduce noise and enhance the image quality. Compared with traditional image enhancement techniques, these deep learning models offer more precise and reliable results, enabling faster and more accurate diagnoses. This study provides insights into the strengths and weaknesses of each model, and aims to contribute to the development of better technologies for quicker and more accurate patient diagnosis.