Hybrid Deep Learning Based Model for Removing Grid-Line Artifacts from Radiographical Images

Abstract

The digital imaging technique known as Computed Radiography (CR) has transformed the medical imaging industry by providing a number of advantages. It eliminates the need for traditional film-based methods, making it more efficient and convenient. A common issue faced with CR images is the presence of grid artifacts and other pattern artifacts, which can have a significant impact on the quality of the images when viewed on a computer screen, especially if a clinic-grade display is not accessible. This paper presents a novel framework for removing grid line artifacts from X-ray images, which is a critical challenge in medical imaging. The framework proposes a hybrid Deep Grid model that combines a Gaussian band-stop filter with ADAM optimization to produce high-quality, grid-line free X-ray images that are suitable for further analysis and diagnosis. Deep learning (DL) models for instance the Convolutional Neural Network (CNN), DenseNet, VGG-Net, and Fast R-CNN were utilized to classify images, and the grid-by-grid removal of grid lines in the image was performed. The proposed framework achieved a high accuracy rate of 98% in eliminating grid line artifacts from X-ray images, demonstrating its possibility for a big improvement the accuracy and reliability of diagnostics for medical based on X-ray images