%T Data Augmentation on Intra-Oral Images Using Image Manipulation Techniques
%A S. Abusalim
%A S.A. Mostafa
%A N. Zakaria
%A S.J. Abdulkadir
%A N. Mokhtar
%I Institute of Electrical and Electronics Engineers Inc.
%P 117-120
%K Learning algorithms; Learning systems, Data augmentation; Deep learning; Fast R-CNN; Image manipulation; Learning models; Machine learning algorithms; Machine-learning; Manipulation techniques; Mean average precision; Training data, Deep learning
%X The quality, quantity, and relevance of training data determine how well most ML models perform, and deep learning models. One of the most frequent problems in implementing machine learning, though, is a lack of data. This is because gathering such data can frequently be expensive and time-consuming. The diversity of training data for machine learning algorithms is increased through data augmentation without the need for new data collection. Basic image manipulation techniques, including horizontal flip, Brightness and contrast, Noise injection, and histogram equalization techniques were used in this work to produce an augmented intraoral dataset. Faster R-CNN, a CNN-based model, was used to analyze the performance of the data augmentation strategies. An extensive simulation shows that the augmented dataset achieves better accuracy than the original dataset. The experimental results show a mean average precision (mAP) of 72.4  on augmentation data. © 2022 IEEE.
%D 2022
%R 10.1109/ICDI57181.2022.10007158
%O cited By 1; Conference of 2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:185994
%J 2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 - Proceedings
%L scholars17299