Dental Material Detection based on Faster Regional Convolutional Neural Networks and Shape Features

Imak, Andaç; Çelebi, Adalet; Türkoğlu, Muammer; Şengür, Abdulkadir

doi:10.1007/s11063-021-10721-5

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…For improving dental disease diagnosis and treatment plans, deep learning [7] has significantly influenced the processing of intraoral X-ray images in image processing [8], segmentation [9][10][11][12] and enhancements [13][14][15]. These developments integrating intraoral X-ray imaging with deep learning techniques enhance the precision of oral health condition recognition and detection, such as dental caries [16][17][18][19][20][21][22], implant [23], oriented tooth [24], restoration by filling [25] and dental material [26]. This study adapts deep learning techniques on a novel dataset to recognize and detect twenty categories as abscessed teeth, calculus, caries, cysts, dental bridges, dental crowns, extracted teeth, filling overhang, impacted wisdom teeth, implants, mesialized dentition, mixed dentition, periodontal bone loss, pulpitis, restoration by filling, retained root, screw-retained restoration, single-root canal treatment, two-root canal treatment and three-root canal treatment.…”

Section: Introductionmentioning

confidence: 99%

CVApool: using null-space of CNN weights for the tooth disease classification

Can,

Isik,

Anagun

2024

Neural Comput & Applic

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In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.

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