Machine Learning Perspectives for Dental Imaging
Vivek K Verma1, Tarun Jain2, Horesh Kumar3
1Vivek K Verma, Department of Computer Science, International Electrotechnical Commission College of Engineering & Technology, India.
2Tarun Jain, Department of Computer Science, International Electrotechnical Commission College of Engineering & Technology, India.
3Horesh Kumar, Department of Computer Science, International Electrotechnical Commission College of Engineering & Technology, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 24 May 2019 | PP: 37-41 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F22080486S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In current decades, an automatic teeth segmentation is an emergent research area in the field of diagnosing dental anomalies and periapical radiographs and also increases the usage of digital records in orthodontics. In digital dental models, the first step is to segment the models or finding the boundaries of each tooth. Earlier, the segmentation of models are done by human operator manually to draw the boundaries, which divides the teeth from each other. Then, the fully automated or ideal segmentation methodologies are developed to interact with the digital dental models. The purpose of this review is to increase the penetration of machine learning methodologies in healthcare; specifically in dental medical images are quiet slow compared to the other research fields. This review also discussed about the advantage of machine learning methodologies for segmenting and classifying the dental medical images and also described about the issues of traditional methods over machine learning based methods in medical imaging.
Keywords: Dental Image, Machine Learning, Image Segmentation, Image Classification, Deep Neural Network, Svm, Knn, Random Forest Classifier.
Scope of the Article: Classification