Abstract
INTRODUCTION: we propose a detection model of gingivitis based on feature extraction based on particle swarm optimization neural network with fractional Fourier entropy. OBJECTIVES: For the sake of reduce the diagnostic burden of doctors' frequent and high concentration. METHODS: Prim
Highlights
Gingivitis is an acute and chronic inflammation of the gingival tissue caused by bacterial infections, foreign irritations and food blockages [1]
Fractional Fourier transform (FRFT) contains features in the frequency and time domain, and features in the domain of frequency described by different sections have different features spectra
Matthews correlation coefficient (MCC) is an equivalently balanced index to measure the possibility of comprehensive consideration of models by dichotomies, and it can be used in the unbalanced samples in some case
Summary
Gingivitis is an acute and chronic inflammation of the gingival tissue caused by bacterial infections, foreign irritations and food blockages [1]. The symptoms are mainly red and swollen gums, swelling and pain, and even bleeding [2]. If not treated in time, it may develop to the deep level and lead to periodontitis. Common inflammation of the gums and chronic periodontitis are diagnosed by measuring the depth of the periodontal probe and showing signs of bleeding during the measurement, and using imaging to assess the loss of alveolar bone. It is possible for the examiner to obtain different results with different probes, and repeated field measurements may cause great pain to the patient. Generations of several new probes have been invented to boost the accuracy of periodontal probe depth measurements.
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