Dental Caries Degree Detection Based on Fuzzy Cognitive Maps and Genetic Algorithm

Haghanifar, Arman; Amirkhani, Abdollah; Mosavi, Mohammad Reza

doi:10.1109/icee.2018.8472687

Cited by 6 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Real-Coded-Genetic-Algorithm was used to avoid above disadvantage in which Real Coded Genetic Algorithmwas used for weights matrix and multilayered FCM model was implemented. [23] Jie Yang, YuchenXieetalin 2018 introducedautomated system for assessing periapical dental radiograph reducing time taken for manual procedures. Input was a radiograph of before and after treatment.…”

Section: Literature Surveymentioning

confidence: 99%

Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis

T.K¹,

Dheeba²

2020

IJRTE

View full text Add to dashboard Cite

Artificial Intelligence, Machine learning, deep learning and image processing is becoming popular in medical sciences. The present digitalized world is remodelling each facetadditionally impacting dentistry and medical field from patient record maintenance, data analysisto new diagnostic methods, novel interference waysand totally different treatment choices. Oral health contributes to various diseases and conditions like Endocarditis, Cardio vascular diseases, diabetes, osteoporosis, pregnancy and birth and many more. Bad breathe, tooth decay, periodontitis, oral abscess, tooth erosion, dentinal sensitivity and many more can be even trickier to detect in plain dental radiography. The most prevalent disease periodontitis is a gum disease when left untreated, leads to tooth loss and more hazardous complications. Early Prediction and Proper diagnosis in time will protect our health from the mentioned diseases which can be implemented by making use of emerging technologies to assist and support dentists in predictions and decision making. Hence focusing more on oral health, In the current paper, the most contributing risk factors and parameters like Pocket Depth, Black Triangles, Alveolar Bone Loss, Furcation, Periodontal Abscess, Smoking, Gingivitis, Clinical Attachment Loss, Mobility Etc. that progresses the disease were taken in to consideration and a Python code was implemented which can be used as a Decision making aid to check whether person suffers or likely to suffer in future or not suffering from the disease.In this paper, literature reviews on the various automated computerized methods used to detect and diagnose the disease were discussed and an attempt was made to clearly identify and describe both the clinical and radiological parameters that a dentist/Periodontist use as a metric to grade/assess the periodontitis. The present strategy can be enhanced as a tool and can be used as a decision making aid by dentists’ in the prediction of periodontitis and can also be used for demonstrating fresher’s or upcoming dentists the progress of gum disease, grading the severity of the disease and the associated risk factors considering clinical, radiological findings and adverse habits thereby improving overall time period taken for manual predictions.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis

T.K¹,

Dheeba²

2020

IJRTE

View full text Add to dashboard Cite

show abstract

“…With regard to the hybrid methods, the FCMs have reacted in two ways. In the first approach, different learning methods have been created by combining evolutionary algorithms with FCMs; such as the training of FCMs by the ant colony optimization 37 and particle swarm optimization, 38 GA, 39 and by evolutionary multitasking. 40 In these cases, only the updating of FCM weights is improved, and we will have the same CFCM.…”

Section: Introductionmentioning

confidence: 99%

A framework for designing cognitive trajectory controllers using genetically evolved interval type‐2 fuzzy cognitive maps

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, a number of research studies have proposed deep learning-based Computer-Aided Diagnosis (CAD) systems to detect dental caries based on various types of data, including clinical assessments [17], infrared light [18], or near-infrared transillumination imaging [19]. Since x-ray radiography is the most common imaging modality in dental clinical practice, the majority of studies have utilized x-rays to develop decision support systems for tooth decay diagnosis.…”

Section: Introductionmentioning

confidence: 99%

PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier

Haghanifar¹,

Majdabadi²,

Ko³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by radiologists relying only on their visual inspection to detect via dental x-rays. In many cases, dental caries is hard to identify using x-rays and can be misinterpreted as shadows due to different reasons such as low image quality. Hence, developing a decision support system for caries detection has been a topic of interest in recent years. Here, we propose an automatic diagnosis system to detect dental caries in Panoramic images for the first time, to the best of authors' knowledge. The proposed model benefits from various pretrained deep learning models through transfer learning to extract relevant features from x-rays and uses a capsule network to draw prediction results. On a dataset of 470 Panoramic images used for features extraction, including 240 labeled images for classification, our model achieved an accuracy score of 86.05% on the test set. The obtained score demonstrates acceptable detection performance and an increase in caries detection speed, as long as the challenges of using Panoramic x-rays of real patients are taken into account. Among images with caries lesions in the test set, our model acquired recall scores of 69.44% and 90.52% for mild and severe ones, confirming the fact that severe caries spots are more straightforward to detect and efficient mild caries detection needs a more robust and larger dataset. Considering the novelty of current research study as using Panoramic images, this work is a step towards developing a fully automated efficient decision support system to assist domain experts.

show abstract

Dental Caries Degree Detection Based on Fuzzy Cognitive Maps and Genetic Algorithm

Cited by 6 publications

References 19 publications

Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis

Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis

A framework for designing cognitive trajectory controllers using genetically evolved interval type‐2 fuzzy cognitive maps

PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier

Contact Info

Product

Resources

About