Geometric Transformations-Based Medical Image Augmentation
S. Kalaivani, N. Asha, A. Gayathri
Abstract
The emergent of machine learning (ML) and deep learning (DL) methods have created a substantial window of chance for their use in the industry. Given that both ML and DL algorithms are capable of identifying links between enormous amounts of data, one of the jobs for which these techniques have the most potential is visual inspection. These methods, nevertheless, call for a lot of photographs, which are not always possible to capture. Techniques for data augmentation are used as a remedy for this. Many datasets are created utilizing data augmentation approaches based on geometric transformations, starting from a very unbalanced dataset containing photos of various medical images. Many datasets are created utilizing data augmentation approaches based on geometric transformations, starting from a very unbalanced dataset containing photos of various medical images. The geometric transformation-based data augmentation segments the infected area and the classification process is proposed to highlight the severity of the disease. The proposed suggests an impartial and all-encompassing framework of evaluation for various information augmentation techniques. With this cutting-edge procedure, various augmented techniques are thoroughly and accurately assessed regarding data diversity and classification accurateness utilizing massive public datasets already available. In order to replicate the typical moderate size datasets used in deep learning, a regular-interval sampling strategy constructed on similarity ranking is described that can be used to choice samples from enormous open datasets and produce a subset that can accurately represent the original set. Then, using a variety of data augmentation methods, the augmented datasets are created from the small-sized datasets.