Ensemble Voting Method to Enhance the Performance of a Dental Caries Detection System using Convolutional Neural Network

Putri  Rizkiah; Maulisa Oktiana; Khairun  Saddami; Maya  Fitria; Fitri  Arnia; Hubbul  Walidainy; Yunida  Yunida

doi:10.35882/jeeemi.v8i2.1343

Putri Rizkiah Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia https://orcid.org/0009-0002-0474-1626
Maulisa Oktiana Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia https://orcid.org/0000-0001-7453-7183
Khairun Saddami Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia https://orcid.org/0000-0001-8344-339X
Maya Fitria Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia https://orcid.org/0000-0002-6593-212X
Fitri Arnia Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia https://orcid.org/0000-0001-6020-1275
Hubbul Walidainy Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia https://orcid.org/0000-0002-5844-9375
Yunida Yunida Department of Electrical and Computer Engineering, Universitas Syiah Kuala, IndonesiaUniversitas Syiah Kuala https://orcid.org/0000-0003-4183-9147

DOI: https://doi.org/10.35882/jeeemi.v8i2.1343

Keywords: Caries detection, Deep learning, Ensemble voting, Hard voting, Soft voting

Abstract

Individual classification models for caries detection still face significant challenges, including limited accuracy and unstable predictions, which can hinder diagnosis, delay clinical decisions, and increase the risks associated with patient care. To overcome these limitations, this study proposes an ensemble voting method that combines five deep learning models, such as ResNet-152, MobileNetV2, InceptionV3, NASNetMobile, and EfficientNet-B5. This approach aims to enhance the accuracy and stability of caries detection by leveraging the complementary strengths of the individual models while mitigating their weaknesses. Each model was trained and tested on the same dataset of dental images, categorized into caries and regular classes. Their predictions were aggregated using hard and soft voting techniques. The ensemble's performance was evaluated using accuracy, precision, recall, and F1-score. The ensemble voting demonstrates a notable improvement in classification performance over individual models. Hard and soft voting have excellent classification performance and consistently outperform the best individual models. The accuracy increased from EfficientNetB5 0.8485 to 0.8864 and 0.8712, representing increases of 4.46% and 2.68%, respectively. The precision increased from MobileNetV2 0.8182 to 0.8493 and 0.8551, representing increases of 3.81% and 4.52%. For recall, EfficientNetB5 ranked highest among individual models with a score of 0.9242. Hard voting increased 1.64% to 0.9394, and soft voting decreased slightly by 3.28% to 0.8939. The F1 score of EfficientNetB5 is 0.8592. Hard and soft voting increased 3.83% and 1.73% to 0.8921 and 0.8741. The proposed ensemble improves the F1-score by 3.83 percentage points compared to the best individual model. The ensemble voting method effectively leverages the complementary strengths of each deep learning model to improve the stability and accuracy of fast, reliable dental caries early detection prediction.

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