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Detecting Periodontal Disease Using Convolutional Neural Networks

Deep Learning has been an emerging machine learning tool in general imaging and computer vision domains, particularly the convolutional neural networks. With its growing interest, it has been applied in different fields of medicine and many different ways. Given the problems and gaps of former studies, this current study aims to find another criterion to detect periodontal disease, aside from intraoral images, gingiva tissue extraction and others. The said study hopes to contribute to both fields of technology and dentistry by recognizing microscopic images of dental plaque, whether it is infected with periodontitis or healthy. The methodology of this current study, dwelled more on classifying which among the microscopic dental plaque images fed into the neural networks were healthy or unhealthy. The current study also aims to determine if the AlexNet architecture is the best fit architectural model using convolutional neural networks through yielding an acceptable accuracy rate and design a model that could automate the process of analyzing the microscopic images obtained. This study used the convolutional neural networks as the classifier and utilized the AlexNet architecture to classify the images, using Tensorflow. With the said methodology of the current study, the model was able to yield an accuracy rate of 75.5% and a mean square error of 0.05348436995.