Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network
Panoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and in...
| Main Authors: | , , , , , |
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| Format: | Article |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9709265/ |
| _version_ | 1819277429728870400 |
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| author | Andac Imak Adalet Celebi Kamran Siddique Muammer Turkoglu Abdulkadir Sengur Iftekhar Salam |
| author_facet | Andac Imak Adalet Celebi Kamran Siddique Muammer Turkoglu Abdulkadir Sengur Iftekhar Salam |
| author_sort | Andac Imak |
| collection | DOAJ |
| description | Panoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and inexperience, manual diagnosis may cause unnoticeable dental caries. Thus, computer-based intelligent vision systems supported by machine learning and image processing techniques are needed to prevent these negativities. This study proposed a novel approach for the automatic diagnosis of dental caries based on periapical images. The proposed procedure used a multi-input deep convolutional neural network ensemble (MI-DCNNE) model. Specifically, a score-based ensemble scheme was employed to increase the achievement of the proposed MI-DCNNE method. The inputs to the proposed approach were both raw periapical images and an enhanced form of it. The score fusion was carried out in the Softmax layer of the proposed multi-input CNN architecture. In the experimental works, a periapical image dataset (340 images) covering both caries and non-caries images were used for the performance evaluation of the proposed method. According to the results, it was seen that the proposed model is quite successful in the diagnosis of dental caries. The reported accuracy score is 99.13%. This result shows that the proposed MI-DCNNE model can effectively contribute to the classification of dental caries. |
| first_indexed | 2024-12-23T23:55:59Z |
| format | Article |
| id | doaj.art-4ccc5a6dbfbf43fd9d6a82d580e09223 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:55:59Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4ccc5a6dbfbf43fd9d6a82d580e092232022-12-21T17:25:15ZengIEEEIEEE Access2169-35362022-01-0110183201832910.1109/ACCESS.2022.31503589709265Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural NetworkAndac Imak0https://orcid.org/0000-0002-3654-040XAdalet Celebi1Kamran Siddique2https://orcid.org/0000-0003-2286-1728Muammer Turkoglu3https://orcid.org/0000-0002-2377-4979Abdulkadir Sengur4https://orcid.org/0000-0003-1614-2639Iftekhar Salam5https://orcid.org/0000-0003-1395-4623Department of Electrical and Electronic Engineering, Faculty of Engineering, Munzur University, Tunceli, TurkeyOral and Maxillofacial Surgery Department, Faculty of Dentistry, Bingol University, Bingol, TurkeyDepartment of Information and Communication Technology, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, MalaysiaDepartment of Software Engineering, Faculty of Engineering, Samsun University, Samsun, TurkeyDepartment of Electrical and Electronic Engineering, Faculty of Technology, Firat University, Elazig, TurkeyDepartment of Information and Communication Technology, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, MalaysiaPanoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and inexperience, manual diagnosis may cause unnoticeable dental caries. Thus, computer-based intelligent vision systems supported by machine learning and image processing techniques are needed to prevent these negativities. This study proposed a novel approach for the automatic diagnosis of dental caries based on periapical images. The proposed procedure used a multi-input deep convolutional neural network ensemble (MI-DCNNE) model. Specifically, a score-based ensemble scheme was employed to increase the achievement of the proposed MI-DCNNE method. The inputs to the proposed approach were both raw periapical images and an enhanced form of it. The score fusion was carried out in the Softmax layer of the proposed multi-input CNN architecture. In the experimental works, a periapical image dataset (340 images) covering both caries and non-caries images were used for the performance evaluation of the proposed method. According to the results, it was seen that the proposed model is quite successful in the diagnosis of dental caries. The reported accuracy score is 99.13%. This result shows that the proposed MI-DCNNE model can effectively contribute to the classification of dental caries.https://ieeexplore.ieee.org/document/9709265/Dental caries detectionscore-based fusiondeep convolutional neural networkclassificationperiapical images |
| spellingShingle | Andac Imak Adalet Celebi Kamran Siddique Muammer Turkoglu Abdulkadir Sengur Iftekhar Salam Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network IEEE Access Dental caries detection score-based fusion deep convolutional neural network classification periapical images |
| title | Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network |
| title_full | Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network |
| title_fullStr | Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network |
| title_full_unstemmed | Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network |
| title_short | Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network |
| title_sort | dental caries detection using score based multi input deep convolutional neural network |
| topic | Dental caries detection score-based fusion deep convolutional neural network classification periapical images |
| url | https://ieeexplore.ieee.org/document/9709265/ |
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