Comparison of Dental Caries Level Images Classification Performance using KNN and SVM Methods

Jusman, Yessi; Anam, Muhammad Khoirul; Puspita, Sartika; Saleh, Edwyn; Kanafiah, Siti Nurul Aqmariah Mohd; Tamarena, Rhesezia Intan

doi:10.1109/icsipa52582.2021.9576774

Cited by 18 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vertical root fracture [72,73] Deep learning Periapical pathosis [21], dental tumors [74], tooth numbering [75][76][77][78], tooth detection and identification [79][80][81], periodontal bone loss [32,82,83] Disease classification Classical image analysis approaches Tooth detection [84,85], osteoporosis assessment [86], dental caries [87] Machine learning Dental caries [88], proximal dental caries [14], molar and pre-molar teeth [89], osteoporosis [90], dental caries [15], periapical lesions [16,17], dental restorations [22], periapical roots [91], teeth with root [92], sagittal patterns [93] Deep learning Tooth numbering [94][95][96][97][98][99], dental implant stages [100], implant fixture [101], bone loss [18], periapical periodontitis [102][103][104][105], dental decay [106], approximal dental caries [19] Disease segmentation C...…”

Section: Disease Detection Machine Learningmentioning

confidence: 99%

“…For osteoporosis assessment using thorax X-ray images, feature extraction was performed using GLCM followed by KNN [ 86 ]. Another early attempt evaluated two machine-learning algorithms; support vector machine (SVM) and K nearest neighbors (KNN) were used for dental caries classification based on features extracted using the GLCM algorithm [ 87 ]. Using machine-learning techniques for diagnosing proximal dental caries, Devito et al utilized an artificial multilayer perceptron neural network reporting an improvement of about 39.4% in dental caries detection with a ROC curve area of 0.884 [ 88 ].…”

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

“…Jusman et al evaluated fine Gaussian SVM and KNN on X-ray images enhanced using the GLCM algorithm for dental caries detection. It was found that fine Gaussian SVM achieves an accuracy of 95.7% for five dental classes [ 87 ]. Using periapical radiographic images, Wu et al developed a program based on image patch histogram classification.…”

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

View full text Add to dashboard Cite

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

show abstract

Section: Disease Detection Machine Learningmentioning

confidence: 99%

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The temporal recovery detection model through trace sequences is presented in Figure 5b. The binary classification between the temporal recovered and original data are performed similarly to [82,84] with 5-fold cross-validation. Video content that was included in the training process for each crf was not part of the testing.…”

Section: Detection Model and Evaluationmentioning

confidence: 99%

Effects on Long-Range Dependence and Multifractality in Temporal Resolution Recovery of High Frame Rate HEVC Compressed Content

Gavrovska

2023

Applied Sciences

View full text Add to dashboard Cite

In recent years, video research has dealt with high-frame-rate (HFR) content. Even though low or standard frame rates (SFR) that correspond to values less than 60 frames per second (fps) are still covered. Temporal conversions are applied accompanied with video compression and, thus, it is of importance to observe and detect possible effects of typical compressed video manipulations over HFR (60 fps+) content. This paper addresses ultra-high-definition HFR content via Hurst index as a measure of long-range dependency (LRD), as well as using Legendre multifractal spectrum, having in mind standard high-efficiency video coding (HEVC) format and temporal resolution recovery (TRR), meaning frame upconversion after temporal filtering of compressed content. LRD and multifractals-based studies using video traces have been performed for characterization of compressed video, and they are mostly presented for advanced video coding (AVC). Moreover, recent studies have shown that it is possible to perform TRR detection for SFR data compressed with standards developed before HEVC. In order to address HEVC HFR data, video traces are analyzed using LRD and multifractals, and a novel TRR detection model is proposed based on a weighted k-nearest neighbors (WkNN) classifier and multifractals. Firstly, HFR video traces are gathered using six constant rate factors (crfs), where Hurst indices and multifractal spectra are calculated. According to TRR and original spectra comparison, a novel detection model is proposed based on new multifractal features. Also, five-fold cross-validation using the proposed TRR detection model gave high-accuracy results of around 98%. The obtained results show the effects on LRD and multifractality and their significance in understanding changes in typical video manipulation. The proposed model can be valuable in video credibility and quality assessments of HFR HEVC compressed content.

show abstract

Deep caries detection using deep learning: from dataset acquisition to detection

Kaur,

Jyoti,

Sharma

et al. 2024

Clin Oral Invest

View full text Add to dashboard Cite

Comparison of Dental Caries Level Images Classification Performance using KNN and SVM Methods

Cited by 18 publications

References 19 publications

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Effects on Long-Range Dependence and Multifractality in Temporal Resolution Recovery of High Frame Rate HEVC Compressed Content

Deep caries detection using deep learning: from dataset acquisition to detection

Contact Info

Product

Resources

About