Research Journal of Pharmacy and Technology

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Using Computer Images to Identify the Pathology of Tooth and the Application of SVM Systems in Dentistry


Affiliations
1 Department of Basic Sciences, College of Dentistry, University of Baghdad, Iraq
     

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Objective: The research aimed to use both CAD and CBST systems and apply them on SVM computer to images to discover the caries in tooth. Materials and Methods: Datasets from different complexities were evaluated using ensemble -SVM algorithm. Depending on sequences and resolutions dataset, the limitation of intra-class variations, to reach significant inter-class variations and background related to the action. We follow the original setup for a pre-defined set of folds. Average accuracy over all classes is reported as performance measure. Results: Images data that was gathered were and using Support Vector Machine (SVM) learning algorithms were proving to end with accurate models based on large feature spaces which were provided by huge dimensional input spaces. Hypothesis space linear functions was used in a high dimensional feature space and combining it with algorithm to optimize and eventually implement it. Conclusion: Dental CAD ans SVM systems are by now capable to speed up the diagnostic procedure and offer a helpful second opinion in doubtful cases.

Keywords

Algorithm, Data, SVM System, Tooth.
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  • Wang CW, Huang CT, Lee JH, Li CH, Chang SW, Siao MJ, Lai TM, Ibragimov B, Vrtovec T, Ronneberger O, Fischer P, Cootes TF and Lindner C. A benchmark for comparison of dental radiography analysis algorithms. Medical Image Analysis. 2016, 31: 63-76. doi: 10.1016/j.media.2016.02.004.

  • Ghaedi L, Gottlieb Z, Sarrett D C, et al. An automated dental caries detection and scoring system for optical images of tooth occlusal surface. Conf Proc IEEE Engineering in Medicine and Biology Society. 2014, 1925-1928. doi: 10.1109/EMBC.2014.6943988.

  • Gayathri V, Hema PM, Narayanan kutty KA. Edge extraction algorithm using linear prediction model on dental x-ray images. International Journal of Computational Applications. 2014, 100(19): 75-87. doi: 10.1109/EMBC.2014.6943988.

  • Benyó B. Identification of dental ischolar_main canals and their medial line from micro-CT and cone-beam CT records. Biomedical Engineering Online. 2012, 11: 81 doi: 10.1186/1475-925X-11-81.

  • Okada K, Rysavy S, Flores A, Linguraru MG. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans. Medical Physics. 2015, 42(4): 1653-1665. doi: 10.1118/1.4914418.

  • Felzenszwalb PF, Girshick RB, McAllester D and Ramanan D. Object detection with discriminatively trained part-based models. Transaction of Pattern Analysis and Machine. 2010, 32(9): 1627–1645. Doi: 10.1109/TPAMI.2009.167

  • Boix X, Gonfaus JM, van de Weijer J, Bagdanov AD, Serrat J and Gonz´alez J. Harmony Potentials Fusing Global and Local Scale for Semantic Image Segmentation. International Journal of Computer Vision. 2012, 96(1): 83–102.

  • Achanta R, Shaji A, Smith K, Lucchi A, Fua P and Susstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. Transaction of Pattern Analysis and Machine. 2012, 34(11): 2274–2282.


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  • Using Computer Images to Identify the Pathology of Tooth and the Application of SVM Systems in Dentistry

Abstract Views: 322  |  PDF Views: 0

Authors

Adnan Mahmood
Department of Basic Sciences, College of Dentistry, University of Baghdad, Iraq

Abstract


Objective: The research aimed to use both CAD and CBST systems and apply them on SVM computer to images to discover the caries in tooth. Materials and Methods: Datasets from different complexities were evaluated using ensemble -SVM algorithm. Depending on sequences and resolutions dataset, the limitation of intra-class variations, to reach significant inter-class variations and background related to the action. We follow the original setup for a pre-defined set of folds. Average accuracy over all classes is reported as performance measure. Results: Images data that was gathered were and using Support Vector Machine (SVM) learning algorithms were proving to end with accurate models based on large feature spaces which were provided by huge dimensional input spaces. Hypothesis space linear functions was used in a high dimensional feature space and combining it with algorithm to optimize and eventually implement it. Conclusion: Dental CAD ans SVM systems are by now capable to speed up the diagnostic procedure and offer a helpful second opinion in doubtful cases.

Keywords


Algorithm, Data, SVM System, Tooth.

References