Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks
Oral cancer is a serious illness, but it is potentially curable if early detection can be achieved successfully. Oral epithelial dysplasia (OED), which is a precursor to oral squamous cell carcinoma (OSCC), can provide abnormal characteristics to diagnose the risk of developing oral cancer. This pap...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/3/216 |
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| author | Watchanan Chantapakul Sirikanlaya Vetchaporn Sansanee Auephanwiriyakul Nipon Theera-Umpon Ritipong Wongkhuenkaew Uklid Yeesarapat Nutchapon Chamusri Mansuang Wongsapai |
| author_facet | Watchanan Chantapakul Sirikanlaya Vetchaporn Sansanee Auephanwiriyakul Nipon Theera-Umpon Ritipong Wongkhuenkaew Uklid Yeesarapat Nutchapon Chamusri Mansuang Wongsapai |
| author_sort | Watchanan Chantapakul |
| collection | DOAJ |
| description | Oral cancer is a serious illness, but it is potentially curable if early detection can be achieved successfully. Oral epithelial dysplasia (OED), which is a precursor to oral squamous cell carcinoma (OSCC), can provide abnormal characteristics to diagnose the risk of developing oral cancer. This paper proposes a neural network architecture for detecting dysplastic features of epithelial architecture, including irregular epithelial stratification and bulbous rete ridges. The different combinations of atrous convolution, batch normalization, global pooling, and dropout are discussed regarding their effects, along with an ablation study. A signature library containing image patches was constructed and utilized to train the models. The best-performing model in the validation set attained an average accuracy of 97.52%. The results of the blind test from the receiver operating characteristic (ROC) curves show that the best model reached the best probability of detection, 0.8571, for irregular epithelial stratifications and 0.8462 for the bulbous rete ridges. |
| format | Article |
| id | doaj-art-7d010deae2b94adc8ebcfd88ff9aa5b1 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-7d010deae2b94adc8ebcfd88ff9aa5b12025-08-20T02:11:22ZengMDPI AGBioengineering2306-53542025-02-0112321610.3390/bioengineering12030216Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural NetworksWatchanan Chantapakul0Sirikanlaya Vetchaporn1Sansanee Auephanwiriyakul2Nipon Theera-Umpon3Ritipong Wongkhuenkaew4Uklid Yeesarapat5Nutchapon Chamusri6Mansuang Wongsapai7Biomedical Engineering Institute, and the Biomedical Engineering and Innovation Research Center, Chiang Mai University, Chiang Mai 50200, ThailandIntercountry Centre for Oral Health, Department of Health, Ministry of Public Health, Chiang Mai 50000, ThailandBiomedical Engineering Institute, and the Biomedical Engineering and Innovation Research Center, Chiang Mai University, Chiang Mai 50200, ThailandBiomedical Engineering Institute, and the Biomedical Engineering and Innovation Research Center, Chiang Mai University, Chiang Mai 50200, ThailandBiomedical Engineering Institute, and the Biomedical Engineering and Innovation Research Center, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai 50200, ThailandIntercountry Centre for Oral Health, Department of Health, Ministry of Public Health, Chiang Mai 50000, ThailandOral cancer is a serious illness, but it is potentially curable if early detection can be achieved successfully. Oral epithelial dysplasia (OED), which is a precursor to oral squamous cell carcinoma (OSCC), can provide abnormal characteristics to diagnose the risk of developing oral cancer. This paper proposes a neural network architecture for detecting dysplastic features of epithelial architecture, including irregular epithelial stratification and bulbous rete ridges. The different combinations of atrous convolution, batch normalization, global pooling, and dropout are discussed regarding their effects, along with an ablation study. A signature library containing image patches was constructed and utilized to train the models. The best-performing model in the validation set attained an average accuracy of 97.52%. The results of the blind test from the receiver operating characteristic (ROC) curves show that the best model reached the best probability of detection, 0.8571, for irregular epithelial stratifications and 0.8462 for the bulbous rete ridges.https://www.mdpi.com/2306-5354/12/3/216bulbous rete ridgesconvolutional neural networkshistopathological imageirregular epithelial stratificationsoral epithelial dysplasia |
| spellingShingle | Watchanan Chantapakul Sirikanlaya Vetchaporn Sansanee Auephanwiriyakul Nipon Theera-Umpon Ritipong Wongkhuenkaew Uklid Yeesarapat Nutchapon Chamusri Mansuang Wongsapai Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks Bioengineering bulbous rete ridges convolutional neural networks histopathological image irregular epithelial stratifications oral epithelial dysplasia |
| title | Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks |
| title_full | Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks |
| title_fullStr | Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks |
| title_full_unstemmed | Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks |
| title_short | Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks |
| title_sort | detection of architectural dysplastic features from histopathological imagery of oral mucosa using neural networks |
| topic | bulbous rete ridges convolutional neural networks histopathological image irregular epithelial stratifications oral epithelial dysplasia |
| url | https://www.mdpi.com/2306-5354/12/3/216 |
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