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Novel technique for caries detection using curvilinear semantic deep convolutional neural network.

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Title: Novel technique for caries detection using curvilinear semantic deep convolutional neural network.
Authors: Rajee, M. V.1 (AUTHOR) rajeemv82@gmail.com, Mythili, C.2 (AUTHOR)
Source: Multimedia Tools & Applications. Mar2023, Vol. 82 Issue 7, p10745-10762. 18p.
Subjects: Convolutional neural networks, Radiographic processing, X-ray imaging, Image processing, Imaging systems
Abstract: Radiography image processing is a technique used for processing radiography images using mathematical operations in which the input is a dental X- ray image or a sequence of X- ray images. The accuracy related segmentation for tooth images forms key point in computer based algorithms. To the great extent image processing techniques has two dimensional images used for processing. The methods of dental X-ray image diagnostic procedure are well established in the dentistry field. This is very useful to the dentist to get extra diagnostic information. Typically, in dental X-ray images, detection of caries and other hard tissues are challenging tasks. These x-ray images have unwanted noises that lead to poor diagnostic information. The main aim of proposed dental image processing system is to remove the unwanted noises at first with the help of robust Hybrid Binary Thresholding with Notch Filter (HBT-NF). The next step with the segmentation of caries, hard tissues from tooth lies with proposed Curvilinear Semantic Deep Convolutional neural network. Finally the teeth are separated from hard tissues and caries at high accuracy of 93.7%. Experimental results on the real dental X-ray images of the proposed system will give better effectiveness compared with other detection methods. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Radiography image processing is a technique used for processing radiography images using mathematical operations in which the input is a dental X- ray image or a sequence of X- ray images. The accuracy related segmentation for tooth images forms key point in computer based algorithms. To the great extent image processing techniques has two dimensional images used for processing. The methods of dental X-ray image diagnostic procedure are well established in the dentistry field. This is very useful to the dentist to get extra diagnostic information. Typically, in dental X-ray images, detection of caries and other hard tissues are challenging tasks. These x-ray images have unwanted noises that lead to poor diagnostic information. The main aim of proposed dental image processing system is to remove the unwanted noises at first with the help of robust Hybrid Binary Thresholding with Notch Filter (HBT-NF). The next step with the segmentation of caries, hard tissues from tooth lies with proposed Curvilinear Semantic Deep Convolutional neural network. Finally the teeth are separated from hard tissues and caries at high accuracy of 93.7%. Experimental results on the real dental X-ray images of the proposed system will give better effectiveness compared with other detection methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2023
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