Classification and detection of dental images using meta-learning

Abstract

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input. Instead of just memorizing a task, this is accomplished through teaching a model how to learn. Algorithms for meta-learning are typically trained on a collection of training problems, each of which has a limited number of labelled instances. Multiple X-ray classification tasks, including the detection of pneumonia, coronavirus disease 2019, and other disorders, have demonstrated the effectiveness of meta-learning. Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods. Due to the high cost and lengthy collection process associated with dental imaging datasets, this is significant for dental X-ray classification jobs. The ability to train models that are more resistant to fresh input is another benefit of meta-learning.

Keywords: Artificial intelligence; Meta-learning; Dental diagnosis; Image segmentation; Medical image interpretation; Dental radiography

Core Tip: Meta-learning offers a promising approach for achieving high-accuracy detection and diagnosis in dental radiographic image classification. This method holds significant promise for accurate and reliable prediction with less bias by leveraging its capability to learn from limited training data and generalize effectively to unseen dental X-ray categories.