Abstract
The success of image-based machine learning and deep learning algorithms depends on the amount and variety of data sets. The richness and heterogeneity of the dataset has a direct impact on the capacity of the model to generalize to new, previously unknown data. Obtaining a large number and variety of images is a very difficult and laborious process, especially for practical applications to be developed in areas such as health, industry and agriculture. If this difficulty cannot be overcome, the performance of machine learning models may decrease or may not reach the desired level at all. This problem may lead to problems such as poor performance or overfitting of the developed models. To solve this problem, data augmentation techniques are used to increase the amount and variety of data sets. These strategies help to provide the necessary data set for the model to learn more successfully. In this publication, we present an image improvement tool that is specifically developed for academics working with limited data sets. This tool uses a variety of data augmentation techniques, including rotation, zooming, panning, noise addition, and blurring, to expand the scale and diversity of available data. This application offers a realistic method for dealing with the issues of limited data availability in machine learning research.
Published Version
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