Intelligent system with dragonfly optimisation for caries detection

Patil, Shashikant; Kulkarni, Vaishali; Bhise, Archana

doi:10.1049/iet-ipr.2018.5442

Cited by 8 publications

(7 citation statements)

References 43 publications

(51 reference statements)

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“…Patil, S. believes that since the mid-1970s, as digital image technology has advanced, computer science and intellectual property science have developed rapidly, and digital image processing has reached even higher levels. In order to understand the outside world, people began to learn how to interpret images from a computer to achieve the same human vision, which is called visual perception or vision [3]. Huo et al argue that fast processors and large memory have become cheaper in recent years as the price of the computer equipment needed for image processing has fallen.…”

Section: Literaturementioning

confidence: 99%

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

2022

Mobile Information Systems

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An intelligent information recognition technology based on picture image processing was proposed to further improve the application effect of intelligent image technology in the color teaching of painting major and improve the level of Chinese painting color teaching with the aid of intelligent image processing technology. Special image recognition technology was used to effectively identify the color of Chinese paintings based on traditional teaching methods. MATLAB software for simulation processing of Chinese painting was also utilized through the fuzzy set theory test sample image recognition. The results show that the method is based on feature vector extraction and orthogonal basis transformation histogram. The former takes 0.072288 seconds in image recognition time, while the latter takes 0.134939 seconds, which is nearly double the former time. Therefore, the software and image processing technology mentioned in the article can further improve the effect and time of image color recognition.

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Section: Literaturementioning

confidence: 99%

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

2022

Mobile Information Systems

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“…In addition, because some methods are not fully described but have been tested on other datasets in other papers, we provide a reference to the appropriate study and provide a description. The comparison table shows that [11,12] have a disappointing performance, whereas [13] performs much better; however, considering the accuracy of 90.00%, the sensitivity of 94.67, and specificity of 63.33%, we can see an imbalance in data as well as a low-performance result. Our proposed method achieved 92.67% specificity compared to the other methods, which is a 29.34% improvement, and the remaining sensitivity values are better than 90%.…”

Section: Experiments and Resultsmentioning

confidence: 97%

“…This section describes the proposed method as well as gives information about our dataset. Since there is no specific well-known public dataset in this field, a carefully prepared dataset is important for evaluating the proposed method; thus, most researchers prefer to build their own datasets for experiments [11][12][13].…”

Section: Methodsmentioning

confidence: 99%

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Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

2021

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Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous state-of-the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.

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“…Patil et al [ 15 ] developed a dragonfly-specific intelligent system. The feature set is extracted using multi-linear principal component analysis (MPCA).…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Bui

Hamamoto

Paing

2022

Entropy

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Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient’s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.

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Intelligent system with dragonfly optimisation for caries detection

Cited by 8 publications

References 43 publications

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

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