  Over the years, the paradigm of medical image analysis has shifted from
manual expertise to automated systems, often using deep learning (DL) systems.
The performance of deep learning algorithms is highly dependent on data
quality. Particularly for the medical domain, it is an important aspect as
medical data is very sensitive to quality and poor quality can lead to
misdiagnosis. To improve the diagnostic performance, research has been done
both in complex DL architectures and in improving data quality using dataset
dependent static hyperparameters. However, the performance is still constrained
due to data quality and overfitting of hyperparameters to a specific dataset.
To overcome these issues, this paper proposes random data augmentation based
enhancement. The main objective is to develop a generalized, data-independent
and computationally efficient enhancement approach to improve medical data
quality for DL. The quality is enhanced by improving the brightness and
contrast of images. In contrast to the existing methods, our method generates
enhancement hyperparameters randomly within a defined range, which makes it
robust and prevents overfitting to a specific dataset. To evaluate the
generalization of the proposed method, we use four medical datasets and compare
its performance with state-of-the-art methods for both classification and
segmentation tasks. For grayscale imagery, experiments have been performed
with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets.
Experimental results demonstrate that with the proposed enhancement
methodology, DL architectures outperform other existing methods. Our code is
publicly available at:
https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement
