Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification Article Swipe
Samah AbuSalim
,
Nordin Zakaria
,
Salama A. Mostafa
,
Yew Kwang Hooi
,
Norehan Mokhtar
,
Said Jadid Abdulkadir
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140678
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140678
In image classification, multi-granularity refers to the ability to classify images with different levels of detail or resolution. This is a challenging task because the distinction between subcategories is often minimal, needing a high level of visual detail and precise representation of the features specific to each class. In dental informatics, and more specifically tooth classification poses many challenges due to overlapping teeth, varying sizes, shapes, and illumination levels. To address these issues, this paper considers various data granularity levels since a deeper level of details can be acquired with increased granularity. Three tooth granularity levels are considered in this study named Two Classes Granularity Level (2CGL), Four Classes Granularity Level (4CGL), and Seven Classes Granularity Level (7CGL) to analyze the performance of teeth detection and classification at multi-granularity levels in Granular Intra-Oral Image (GIOI) dataset. Subsequently, a Faster Region-Convolutional Neural Network (FR-CNN) based on three ResNet models is proposed for teeth detection and classification at multi-granularity levels from the GIOI dataset. The FR-CNN-ResNet models exploit the effect of the tooth classification granularity technique to empower the models with accurate features that lead to improved model performance. The results indicate a remarkable detection effect in investigating the granularity effect on the FR-CNN-ResNet model's performance. The FR-CNN-ResNet-50 model achieved 0.94 mAP for 2CGL, 0.74 mAP for 4CGL, and 0.69 mAP for 7CGL, respectively. The findings demonstrated that multi-granularity enables flexible and nuanced analysis of visual data, which can be useful in a wide range of applications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2023.0140678
- http://thesai.org/Downloads/Volume14No6/Paper_78-Multi_Granularity_Tooth_Analysis_via_Faster_Region_Convolutional.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383424131
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383424131Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2023.0140678Digital Object Identifier
- Title
-
Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Samah AbuSalim, Nordin Zakaria, Salama A. Mostafa, Yew Kwang Hooi, Norehan Mokhtar, Said Jadid AbdulkadirList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2023.0140678Publisher landing page
- PDF URL
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https://thesai.org/Downloads/Volume14No6/Paper_78-Multi_Granularity_Tooth_Analysis_via_Faster_Region_Convolutional.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume14No6/Paper_78-Multi_Granularity_Tooth_Analysis_via_Faster_Region_Convolutional.pdfDirect OA link when available
- Concepts
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Granularity, Computer science, Convolutional neural network, Artificial intelligence, Pattern recognition (psychology), Data mining, Contextual image classification, Machine learning, Image (mathematics), Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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