Enhancement of Chest X-ray Images to Improve Screening Accuracy Rate using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier

Chest X-ray images are usually used to identify the causes of patients symptoms, including the classes of lung or heart disorders. In visualization examination, CXR imaging in anterior posterior views is a preliminary screening method used by clinicians or radiologists to diagnose possible lung abnormalities. However, the identification of the causes of multiple abnormalities associated with coexisting conditions presents a challenge. In ruling out a suspected lung disease, the signs and symptoms of physical conditions need to be identified to arrive at a definitive diagnosis. Hence, this study aims to propose an iterated function system (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CXR images and to improve screening accuracy. For digital image processes, a two-dimensional (2D) fractional-order convolution is used to enhance symptomatic features. The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. The accuracy rate is thus improved. The proposed classifier is evaluated in terms of recall (99.6%), precision (87.78%), accuracy (88.88%), and F1 score (0.9334).