Deep Analysis of Medical Image Augmentation Using Pixellevel Transformation Techniques

The exponential elevation of data-oriented technologies and deep learning models in recent years had a major impact on several fields, with image data being one of the primary areas of interest. Using a variety of imaging techniques and procedures, medical imaging produces images of the human body to diagnose and treat patients. Medical applications such as MRI, CT, ultrasound, X-ray, and PET imaging are used in the medical field. The shortage of medical image data is an important challenge owing to privacy issues, regulatory constraints, high cost, and the necessity for specialist annotations. In medical imaging, the growth and integrity of deep learning (DL) algorithms are hindered by low-quality images, limited access to diverse datasets, and a lack of longitudinal data. Augmentation techniques improve training data by introducing modifications not found in the original dataset. This bigger dataset reduces overfitting, enhances model generalization, and increases accuracy and dependability in practical applications. Within the scope of augmentation, the following techniques, such as geometric transformation, neural style transfer, adversarial training, data augmentation by GANs, and pixel-level transformation, are the most notable techniques. In medical imaging, the enhancement of the model's robustness may be attributed to pixel-level transformations, which include brightness modification, alteration of contrast, noise introduction, blurring, grayscale conversion, histogram equalization, gamma correction, and saturation. The model’s generalization and robustness are improved by this method. By producing a variety of training samples, pixel-level transformations improve performance on unseen data. This paper provides an overview of pixel-level transformation-based image augmentation techniques.