Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks
Abstract Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distinguishing and detection of oral carcinoma and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-06318-w |
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| author | Kritsasith Warin Wasit Limprasert Teerawat Paipongna Sitthi Chaowchuen Sothana Vicharueang |
| author_facet | Kritsasith Warin Wasit Limprasert Teerawat Paipongna Sitthi Chaowchuen Sothana Vicharueang |
| author_sort | Kritsasith Warin |
| collection | DOAJ |
| description | Abstract Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distinguishing and detection of oral carcinoma and sarcoma in contrast-enhanced CT images. This study included 3,259 slices of CT images of oral cancer cases from the cancer hospital and two regional hospitals from 2016 to 2020. Multiclass classification models were constructed using DenseNet-169, ResNet-50, EfficientNet-B0, ConvNeXt-Base, and ViT-Base-Patch16-224 to accurately differentiate between oral carcinoma and sarcoma. Additionally, multiclass object detection models, including Faster R-CNN, YOLOv8, and YOLOv11, were designed to autonomously identify and localize lesions by placing bounding boxes on CT images. Performance evaluation on a test dataset showed that the best classification model achieved an accuracy of 0.97, while the best detection models yielded a mean average precision (mAP) of 0.87. In conclusion, the CNN-based multiclass models have a great promise for accurately determining and distinguishing oral carcinoma and sarcoma in CT imaging, potentially enhancing early detection and informing treatment strategies. |
| format | Article |
| id | doaj-art-e9a31000baad4db798dcc8eb19fb221f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e9a31000baad4db798dcc8eb19fb221f2025-08-20T03:03:28ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-06318-wDetermination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networksKritsasith Warin0Wasit Limprasert1Teerawat Paipongna2Sitthi Chaowchuen3Sothana Vicharueang4Faculty of Dentistry, Thammasat UniversityCollege of Interdisciplinary Studies, Thammasat UniversitySakon Nakhon HospitalUdonthani Cancer HospitalStoreMesh, Thailand Science ParkAbstract Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distinguishing and detection of oral carcinoma and sarcoma in contrast-enhanced CT images. This study included 3,259 slices of CT images of oral cancer cases from the cancer hospital and two regional hospitals from 2016 to 2020. Multiclass classification models were constructed using DenseNet-169, ResNet-50, EfficientNet-B0, ConvNeXt-Base, and ViT-Base-Patch16-224 to accurately differentiate between oral carcinoma and sarcoma. Additionally, multiclass object detection models, including Faster R-CNN, YOLOv8, and YOLOv11, were designed to autonomously identify and localize lesions by placing bounding boxes on CT images. Performance evaluation on a test dataset showed that the best classification model achieved an accuracy of 0.97, while the best detection models yielded a mean average precision (mAP) of 0.87. In conclusion, the CNN-based multiclass models have a great promise for accurately determining and distinguishing oral carcinoma and sarcoma in CT imaging, potentially enhancing early detection and informing treatment strategies.https://doi.org/10.1038/s41598-025-06318-w |
| spellingShingle | Kritsasith Warin Wasit Limprasert Teerawat Paipongna Sitthi Chaowchuen Sothana Vicharueang Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks Scientific Reports |
| title | Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks |
| title_full | Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks |
| title_fullStr | Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks |
| title_full_unstemmed | Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks |
| title_short | Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks |
| title_sort | determination of the oral carcinoma and sarcoma in contrast enhanced ct images using deep convolutional neural networks |
| url | https://doi.org/10.1038/s41598-025-06318-w |
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