Transfer Learning Based Automatic Human Identification using Dental Traits- An Aid to Forensic Odontology

Sathya, B.; Neelaveni, R.

doi:10.1016/j.jflm.2020.102066

Cited by 15 publications

(4 citation statements)

References 17 publications

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“…To create an accurate model of CNN algorithm, we require a substantial amount of data, which we refer to as 'big data' [27]. The term 'big data' refers to large amounts of digital information, including photos and reports, that are stored in electronic formats.…”

Section: Discussionmentioning

confidence: 99%

Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research

Handayani,

Yudianto,

M.A.R

et al. 2024

AOS

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Background: The use of cephalometric pictures in dental radiology is widely acknowledged as a dependable technique for determining the gender of an individual. The Visual Geometry Group 16 (VGG16) and Visual Geometry Group 19 (VGG19) algorithms have been proven to be effective in image classification. Objectives: To acknowledge the importance of comprehending the complex procedures associated with the generation and adjustment of inputs in order to obtain precise outcomes using the VGG16 and VGG19 algorithms. Material and Method: The current work utilised a dataset including 274 cephalometric radiographic pictures of adult Indonesians’ oral health records to construct a gender classification model using the VGG16 and VGG19 architectures using Python. Result: The VGG16 model has a gender identification accuracy of 93% for females and 73% for males, resulting in an average accuracy of 89% across both genders. In the context of gender identification, the VGG19 model has been found to achieve an accuracy of 0.95% for females and 0.80% for men, resulting in an overall accuracy of 0.93% when considering both genders. Conclusion: The application of VGG16 and VGG19 models has played a significant role in identifying gender based on the study of cephalometric radiography. This application has demonstrated the exceptional effectiveness of both models in accurately predicting the gender of Indonesian adults.

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Section: Discussionmentioning

confidence: 99%

Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research

Handayani,

Yudianto,

M.A.R

et al. 2024

AOS

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“…Sathya and Neelaveni [ 17 ] identify and number teeth in radiographic images with a transfer learning approach using AlexNet with TL. This study achieved an accuracy on molar teeth of 94.16% and 94.06%, in premolars of 93.75% and 94.25%, in canines of 86.5% and 87%, and in incisors of 91.5% and 89.5% in maxilla and mandible, respectively.…”

Section: Discussionmentioning

confidence: 99%

A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images

Prados-Privado

Villalón²,

Torres³

et al. 2021

BioMed Research International

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Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions.

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“…Se encontraron ocho en inglés. Para reducir el tiempo de reconocimiento con el empleo de radiografías panorámicas Sathya et al 20 utilizaron el modelo Alex Net desarrollado por Alex Krizhevsky que lograba identificar a una persona usando sólo una pieza dentaria mostrando una precisión de 95% en incisivos, 94,5% en caninos y 97,5% en premolares y molares.…”

Section: Revisión Y Discusiónunclassified

Aplicación de la inteligencia artificial en Odontología

Cacñahuaray-Martínez

Gómez-Meza

Lamas-Lara

et al. 2021

Odontol Sanmarquina

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En este artículo se revisó sobre inteligencia artificial (IA) y su aplicación en el campo odontológico. El objetivo fue revisar la evidencia sobre inteligencia artificial y su aplicación en las diferentes especialidades odontológicas. Se realizó una búsqueda en las bases de datos de Medline/PubMed, Scopus y Web of Science desde el año 2017 al 2021. Se encontraron 321 artículos, de los cuales se seleccionaron 62, que mostraron la aplicación de la IA en seis especialidades: periodoncia, implantología, odontología forense, medicina y patología oral, ortodoncia y diagnóstico/cariología/endodoncia. Se encontró que la especialidad con más artículos sobre el tópico de IA fue la de diagnóstico/cariología/endodoncia y el tipo de inteligencia artificial que más se utilizó fue el de red neural convolucional. Se concluyó que la IA se está utilizando ampliamente en odontología demostrando resultados prometedores ya que muestran una precisión equivalente a la de diferentes especialistas capacitados y en algunos casos superan los errores humanos demostrando excelentes resultados.

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Transfer Learning Based Automatic Human Identification using Dental Traits- An Aid to Forensic Odontology

Cited by 15 publications

References 17 publications

Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research

Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research

A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images

Aplicación de la inteligencia artificial en Odontología

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