Deep convolutional neural network designed for age assessment based on orthopantomography data

Kahaki, Seyed Mostafa Mousavi; Nordin, Mohamad Sahari; Ahmad, Noor Hazlina; Arzoky, Mahir; Ismail, Waidah

doi:10.1007/s00521-019-04449-6

Cited by 35 publications

(29 citation statements)

References 21 publications

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“…Tuzoff et al reported a method of tooth detection and numbering with orthopantomography using simple CNNs, which could help save time and improve the process of filling out dental charts 24 . Kahaki et al tried to establish an age estimation method based on global fuzzy segmentation and local feature extraction using a projection-based feature transform and a designed deep CNN model; the molars were isolated from 456 young participants' orthopantomography as an analysis target 25 . Schwendicke et al performed a scoping review of CNNs for dental image diagnostics and concluded that CNNs may be used in diagnostic-assistance systems in the dental field 26 .…”

Section: Discussionmentioning

confidence: 99%

Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study

Matsuda

Miyamoto

Yoshimura

et al. 2020

Sci Rep

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Forensic dental examination has played an important role in personal identification (PI). However, PI has essentially been based on traditional visual comparisons of ante-and postmortem dental records and radiographs, and there is no globally accepted PI method based on digital technology. Although many effective image recognition models have been developed, they have been underutilized in forensic odontology. The aim of this study was to verify the usefulness of PI with paired orthopantomographs obtained in a relatively short period using convolutional neural network (CNN) technologies. Thirty pairs of orthopantomographs obtained on different days were analyzed in terms of the accuracy of dental PI based on six well-known CNN architectures: VGG16, ResNet50, Inception-v3, InceptionResNet-v2, Xception, and MobileNet-v2. Each model was trained and tested using paired orthopantomographs, and pretraining and fine-tuning transfer learning methods were validated. Higher validation accuracy was achieved with fine-tuning than with pretraining, and each architecture showed a detection accuracy of 80.0% or more. The VGG16 model achieved the highest accuracy (100.0%) with pretraining and with fine-tuning. This study demonstrated the usefulness of CNN for PI using small numbers of orthopantomographic images, and it also showed that VGG16 was the most useful of the six tested CNN architectures.

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Section: Discussionmentioning

confidence: 99%

Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study

Matsuda

Miyamoto

Yoshimura

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Thanks to the rapid development of flexible vision sensors and visual sensor networks, computer vision has entered a new phase. The improvements in computer vision-derived applications (such as vehicle tracking [1], facial interactions [2] and age assessment [3]) and various coding standards [4] also feed back the development of vision sensors. Vision sensors need the support of underlying .…”

Section: Introductionmentioning

confidence: 99%

Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation

Zhang

Chu

Leng

et al. 2020

Sensors

154

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With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.

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“…The importance of age estimation may also be useful for civil, criminal, law enforcement, airport security and for forensic purposes. In a similar study on the same Malaysian children dataset, the automatic age assessment [15] was proposed based on pre-trained deep convolution neural network. The results of this approach concluded that the method can efficiently classify the images with high accuracy and precision.…”

Section: Introductionmentioning

confidence: 99%

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

et al. 2020

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Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

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Deep convolutional neural network designed for age assessment based on orthopantomography data

Cited by 35 publications

References 21 publications

Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study

Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study

Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

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