Abstract
Image processing, a branch of computer science, has grown exponentially over the years. It is a collection of computational techniques for analyzing, enhancing, compressing, and reconstructing images. Medical images are specialized images used for specific purposes. Each medical image dataset may need a image processing technique to enhance and extract a feature, specific for a particular diagnosis. Here Datasets of 4–16–year-olds comprising of (orthopantomograms) OPG images is utilized to identify tooth stage. Using a hybrid of two feature extraction techniques, a challenge linked to identifying developmental stages of the tooth was successfully solved. KNN and SVM classifiers were compared using feature extraction to identify the stage of tooth development. In this research work, 97.14% of accuracy in identifying the stage of the tooth development was achieved using SVM classifier. Estimating the age using this hybrid feature extraction and SVM classifier achieved more than 90% accuracy.
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Stella, A., Selvi, T. (2022). Age Estimation in Digital Radiograph Using HOG and DWT Feature Extraction. In: Pundir, A.K.S., Yadav, N., Sharma, H., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1324-2_16
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DOI: https://doi.org/10.1007/978-981-19-1324-2_16
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