Hybrid Learning System-Based Dental Caries Detection in X-Ray Images: Comparing Accuracy with Support Vector Machine

R. Vijay, G. Ramkumar

2023

Abstract

The primary objective of this study is to conduct a comparison between the accuracy of Support Vector Machines (SVM) and a Novel Hybrid Learning System (Novel HLS) for the detection of dental caries in dental photos obtained from a dedicated dataset. In this investigation, a total of 86 samples were gathered and divided into two distinct groups. Specifically, Group 1 comprised 43 samples that were processed using the Novel HLS approach, while Group 2 consisted of 43 samples that underwent processing with the SVM method. The dataset was imported as per the research protocol, and the Novel HLS code was developed employing Google Colab software. To determine the sample size, an online statistical analysis tool was employed, aiming for an 80% pretest power and an alpha value of 0.05. The sample size was calculated based on prior research findings. Results revealed that SVM achieved an accuracy rate of 70.816%, while the novel HLS method demonstrated a significantly higher accuracy of 97.221%. A statistical significance level of 0.012 (P < 0.05) indicated that there exists a noteworthy disparity in accuracy between the two methods. The dataset substantiates the observation that the Novel HLS approach outperforms SVM by a significant margin in terms of its predictive capabilities for dental caries detection.

Paper Citation

in Harvard Style

Vijay R. and Ramkumar G. (2023). Hybrid Learning System-Based Dental Caries Detection in X-Ray Images: Comparing Accuracy with Support Vector Machine. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 122-127. DOI: 10.5220/0012572000003739

in Bibtex Style

@conference{ai4iot23,
author={R. Vijay and G. Ramkumar},
title={Hybrid Learning System-Based Dental Caries Detection in X-Ray Images: Comparing Accuracy with Support Vector Machine},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={122-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012572000003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Hybrid Learning System-Based Dental Caries Detection in X-Ray Images: Comparing Accuracy with Support Vector Machine
SN - 978-989-758-661-3
AU - Vijay R.
AU - Ramkumar G.
PY - 2023
SP - 122
EP - 127
DO - 10.5220/0012572000003739
PB - SciTePress