relation: https://khub.utp.edu.my/scholars/17299/
title: Data Augmentation on Intra-Oral Images Using Image Manipulation Techniques
creator: Abusalim, S.
creator: Mostafa, S.A.
creator: Zakaria, N.
creator: Abdulkadir, S.J.
creator: Mokhtar, N.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Abusalim, S. and Mostafa, S.A. and Zakaria, N. and Abdulkadir, S.J. and Mokhtar, N.  (2022) Data Augmentation on Intra-Oral Images Using Image Manipulation Techniques.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146985629&doi=10.1109%2fICDI57181.2022.10007158&partnerID=40&md5=dd6ac7948944a7d79edb4a0edcb8e56e
relation: 10.1109/ICDI57181.2022.10007158
identifier: 10.1109/ICDI57181.2022.10007158