ADGRU: Adaptive DenseNet with gated recurrent unit for automatic diagnosis of periodontal bone loss and stage periodontitis with tooth segmentation mechanism

Abstract

Background: Periodontics and gingivitis are two of the most widely prevalent illnesses that affect people nowadays. The sixth most common disease in the world is periodontitis, and detecting periodontal bone loss is essential in the earlier condition and is crucial for the development of the proper diagnosis. Early bone loss detection can be assisted by using computer-assisted radiography examination. Understanding disease progression helps to select the most effective treatment action.

Objectives: An effective deep model is suggested to detect periodontal bone loss at an earlier stage for preventing the progression of Periodontics bone loss.

Methods: This work is intimated by collecting images from online resources. Further, the images gathered from the dataset are preceded by the tooth segmentation which is done using DenseUNet + + . Further, the segmented images are given to the Adaptive DenseNet with Gated Recurrent Unit (AD-GRU) for detecting periodontal bone loss and this diagnosis is used for the periodontitis stage, where the ADGRU performance is augmented by optimizing the attributes using the Refined Red Kite Optimization Algorithm (RRKOA).

Results: The offered approach attained an accuracy of 94.45% which is higher than the88.63%, 90.58%, 89.54%, and 92.96% attained by the LSTM, DenseNet, GRU, DenseNet-GRU.

Data conclusion: The findings of the simulation proved the designed framework outperformed the traditional model with high accuracy.

Clinical relevance: The developed effectual deep model-based periodontal bone loss and stage periodontitis diagnosis structure is used in healthcare applications.

Keywords: Adaptive Densenet with Gated Recurrent Unit; DenseUNet++; Periodontal Bone Loss; Refined Red Kite Optimization Algorithm; Stage Periodontitis; Tooth Segmentation.