eprintid: 17299 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/72/99 datestamp: 2023-12-19 03:23:43 lastmod: 2023-12-19 03:23:43 status_changed: 2023-12-19 03:07:49 type: conference_item metadata_visibility: show creators_name: Abusalim, S. creators_name: Mostafa, S.A. creators_name: Zakaria, N. creators_name: Abdulkadir, S.J. creators_name: Mokhtar, N. title: Data Augmentation on Intra-Oral Images Using Image Manipulation Techniques ispublished: pub keywords: 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 note: 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 abstract: 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. date: 2022 publisher: Institute of Electrical and Electronics Engineers Inc. official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146985629&doi=10.1109%2fICDI57181.2022.10007158&partnerID=40&md5=dd6ac7948944a7d79edb4a0edcb8e56e id_number: 10.1109/ICDI57181.2022.10007158 full_text_status: none publication: 2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 - Proceedings pagerange: 117-120 refereed: TRUE isbn: 9798350397000 citation: 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.