Detection of concha bullosa using deep learning models in cone-beam computed tomography images: a feasibility study Article Swipe
Shishir Shetty
,
Auwalu Saleh Mubarak
,
Leena R. David
,
Mhd Omar Al Jouhari
,
Wael Talaat
,
Sausan Al Kawas
,
Natheer Al‐Rawi
,
Sunaina Shetty Yadadi
,
Mamatha Shetty
,
Dilber Uzun Ozsahin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7181/acfs.2024.00283
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7181/acfs.2024.00283
Background: Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images.Methods: Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT.Results: CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF.Conclusion: Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7181/acfs.2024.00283
- OA Status
- diamond
- Cited By
- 4
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408009233Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7181/acfs.2024.00283Digital Object Identifier
- Title
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Detection of concha bullosa using deep learning models in cone-beam computed tomography images: a feasibility studyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-20Full publication date if available
- Authors
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Shishir Shetty, Auwalu Saleh Mubarak, Leena R. David, Mhd Omar Al Jouhari, Wael Talaat, Sausan Al Kawas, Natheer Al‐Rawi, Sunaina Shetty Yadadi, Mamatha Shetty, Dilber Uzun OzsahinList of authors in order
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https://doi.org/10.7181/acfs.2024.00283Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.7181/acfs.2024.00283Direct OA link when available
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Concha bullosa, Medicine, Cone beam computed tomography, Computed tomography, Cone (formal languages), Radiology, Artificial intelligence, Algorithm, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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