Detection of Dental Diseases using Classification Algorithms
K.Manjula1, G.Durga Devi2, K.Vijayarekha3
1K.Manjula, School of CSE, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, (Tamil Nadu), India.
2G.Durga Devi, School of CSE, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, (Tamil Nadu), India.
3K.Vijayarekha, School of EEE, SASTRA Deemed University, Thanjavur, (Tamil Nadu), India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 4485-4489 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6808098319/2019©BEIESP | DOI: 10.35940/ijrte.C6808.098319
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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: Dental diseases may be caused if the food taken stays in the corners of the mouth. It is important to analyze the dental images to improve and qualify medical images for correct diagnosis. The teeth abnormalities may fall into different categories such as dental implants, gum diseases, crack, bone grafting, and root canal. This work aims to identify the type of abnormalities using classification algorithms — image Processing Techniques, namely Enhancement, Segmentation, and Classification involved in this process of dental disease detection. Decorrelation Stretch, Wiener Filter, and Contrast Enhancement are some of the enhancement techniques which were used to improve the clarity of a dental image. Edge Detection, Otsu’s Threshold, Region-Based Segmentation, and Texture filters are few of the image segmentation techniques. These are used to identify the defected area of an image, and then the type of abnormalities was classified using K-NN and SVM.
Keywords- Decorrelation Stretch, Edge Detection, K-Nearest Neighbor, Region of Interest.
Scope of the Article: Classification