| Autor: |
Latke, Vaishali, Narawade, Vaibhav |
| Předmět: |
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| Zdroj: |
Grenze International Journal of Engineering & Technology (GIJET); 2023, Vol. 9 Issue 2, p396-402, 7p |
| Abstrakt: |
Dentists detect abnormalities in tooth with dental X-ray imaging techniques. Due to a wide variation in human tooth shapes, sizes, and abnormalities, processing a dental image is difficult and time-consuming task. Evaluation of dental anomalies using manual observation and inference remains to be a challenge till date. In order to make perfect analysis and formulate suitable treatment plan, automation in the field of dental picture segmentation and evaluation is crucially important. On reviewing a few approaches in the existing literature, a hybrid model has been proposed to detect Periapical lesions from an input X-ray image. Research has been segregated into three primary areas viz. Image Processing, Machine Learning and Deep Learning methodologies. The model has been tested on several X-ray images and accuracy of the same has been ascertained. Future research scope towards refinement of endodontic treatment has been proposed. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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