Enhanced chest X-ray detection with fractal pixel transformation technique
Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection
Computer Vision and Pattern Recognition
Summary
Detecting lung diseases from chest X-rays is hard because images can be unclear and noisy. The authors created a new method that brightens the important parts of the image and uses special fractal math shapes to highlight lung features. Then, a smart computer system sorts the images to identify different lung illnesses accurately. Their tests showed this method works better than previous approaches in spotting problems in chest X-rays.
What this means in practice
- •For radiology departments: Use enhanced image processing and fractal-based analysis to improve automatic detection of lung diseases in chest X-rays, aiding radiologists in diagnosis.
- •For medical device manufacturers: Incorporate the exponential pixelating integral method into imaging software to build more precise diagnostic tools for respiratory disorders.$Commercial implications: Enables development of advanced X-ray analysis software that improves accuracy and speed of lung disease diagnosis for hospitals and clinics.
Authors
Naveenraj Kamalakannan, Sri Ram Macharla, M Kanimozhi, M S Sudhakar
Abstract
The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45% and 96.53-98.10% respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.