Multi-label Classification of Panoramic Radiographic Images Using a Convolutional Neural Network

• Autor(a) principal: Campos, Leonardo S. [UNESP]
• Data de Publicação: 2020
• Outros Autores: Salvadeo, Denis H. P. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-030-64556-4_27; http://hdl.handle.net/11449/205639
• topic: Convolutional neural network; Dentistry images; Image classification; Panoramic radiography

Dentistry is one of the areas which mostly present potential for application of machine learning techniques, such as convolutional neural networks (CNNs). This potential derives from the fact that several of the typical diagnosis methods on dentistry are based on image analysis, such as diverse types of X-ray images. Typically, these analyses require an empiric and specialized assessment by the professional. In this sense, machine learning can contribute with tools to aid the professionals in dentistry, such as image classification, whose objective is to classify and identify patterns and classes on a set of images. The objective of this current study is to develop an algorithm based on a convolutional neural network with the skill to identify independently six specific classes on the images and classify them accordingly on panoramic X-ray images, also known as orthopantomography. The six independent classes are: Presence of all 28 teeth, restoration, braces, dental prosthesis, images with more than 32 teeth and images with missing teeth. The workflow was based on a DOE (Design of experiments) study, considering the neural network architecture variables as factors, in order to identify the most significant ones, which ones mostly contribute to improve the fitness of the network, and the interactions between these in order to optimize the network architecture, based on the F1 and recall scores. Obtained results are promising, considering that for the optimal network architecture, F1 and Recall scores of 87% and 86%, respectively, were obtained.