Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms

TL;DR

This paper presents automated algorithms to enhance the quality of medical X-ray images, improving diagnostic accuracy by addressing artifacts and noise, especially useful for older or faulty scanning equipment.

Contribution

It introduces automated image quality enhancement methods specifically adapted for medical X-ray scans, with benchmarking on standard datasets to improve diagnostic visualization.

Findings

Certain algorithms significantly improve image quality

Enhanced images facilitate better diagnostic accuracy

Algorithms outperform existing methods in benchmark tests

Abstract

In clinical diagnosis, diagnostic images that are obtained from the scanning devices serve as preliminary evidence for further investigation in the process of delivering quality healthcare. However, often the medical image may contain fault artifacts, introduced due to noise, blur and faulty equipment. The reason for this may be the low-quality or older scanning devices, the test environment or technicians lack of training etc; however, the net result is that the process of fast and reliable diagnosis is hampered. Resolving these issues automatically can have a significant positive impact in a hospital clinical workflow, where often, there is no other way but to work with faulty/older equipment or inadequately qualified radiology technicians. In this paper, automated image quality improvement approaches for adapted and benchmarked for the task of medical image super-resolution. During experimental evaluation on standard open datasets, the observations showed that certain algorithms perform better and show significant improvement in the diagnostic quality of medical scans, thereby enabling better visualization for human diagnostic purposes.