Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features

Jusman, Yessi; Anam, Muhammad Khoirul; Puspita, Sartika; Saleh, Edwyn

doi:10.1109/isemantic52711.2021.9573208

Cited by 12 publications

(2 citation statements)

References 14 publications

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“…The main objective of this study is to develop an automated classification system that can accurately identify these plants using their leaf images. This system proposes the use of the Canny [2], [3] method for image segmentation and Hu Moments [4] for feature extraction, coupled with the K-Nearest Neighbors (K-NN) [5] algorithm for classification. By integrating these techniques, the study seeks to enhance the precision and efficiency of plant classification.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm

Hayatou Oumarou,

Rismayanti

2024

ijodas

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The study "Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm" investigates the potential of image processing and machine learning techniques in the classification of empon plants, specifically ginger and turmeric. Utilizing a dataset of leaf images, the research employed the Canny method for image segmentation and Hu Moments for feature extraction, followed by classification using the K-Nearest Neighbors (K-NN) algorithm. The performance of the model was evaluated through a 5-fold cross-validation method, focusing on metrics such as accuracy, precision, recall, and F1-score. The results showcased the model's variable performance, with the highest accuracy reaching 65.33%. The study contributes to the field by demonstrating the application of Hu Moments in plant classification and by assessing the K-NN algorithm's effectiveness in this context. These findings offer insights into the potential of combining image processing techniques with machine learning for accurate plant classification, paving the way for further research in the area.

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Section: Introductionmentioning

confidence: 99%

Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm

Hayatou Oumarou,

Rismayanti

2024

ijodas

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“…In [ 17 ], a system for predicting dental caries was developed using Laplacian filtering, window-based adaptive thresholding, morphology, statistical features, and a backpropagation neural network. In [ 18 ], Hu’s moment was used to train support vector machine and k-nearest neighbors for the classification of four levels of dental caries. In [ 19 ], both raw periapical images and the enhanced images were the inputs of an ensemble deep convolutional neural network model for dental caries detection.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images

Chen,

Yang,

Chen

et al. 2023

Bioengineering

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Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is automatically detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.

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Caries level classification based on Zernike moment invariant and machine learning

Jusman,

Aini,

Chamim

et al. 2023

Proceedings of the 9th International Symposium on Innovative Bioproduction Indonesia on Biotechnology and Bioengineering 2022:

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Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features

Cited by 12 publications

References 14 publications

Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm

Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm

Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images

Caries level classification based on Zernike moment invariant and machine learning

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