CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning
Abstract Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accurate differential diagnosis of 14 common o...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03268-1 |
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| author | Afnan Al-Ali Ali Hamdi Mohamed Elshrif Keivin Isufaj Khaled Shaban Peter Chauvin Sreenath Madathil Ammar Daer Faleh Tamimi Raidan Ba-Hattab |
| author_facet | Afnan Al-Ali Ali Hamdi Mohamed Elshrif Keivin Isufaj Khaled Shaban Peter Chauvin Sreenath Madathil Ammar Daer Faleh Tamimi Raidan Ba-Hattab |
| author_sort | Afnan Al-Ali |
| collection | DOAJ |
| description | Abstract Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accurate differential diagnosis of 14 common oral lesions—benign, pre-malignant, and malignant—across various mouth locations using photographic images. A dataset of 2,072 clinical images was used to train and validate the model. The proposed framework integrates EfficientNet-B3 for classification and ResNet-101-based Mask R-CNN for segmentation, achieving a classification accuracy of 74.49% and segmentation performance with an average precision (AP50) of 72.18. The gradient-weighted class activation map technique was applied to the model outputs to enable visualization of the specific areas that were most influential for predictive decisions made by the model. This significantly improves upon the state-of-the-art, where previous models achieved lower segmentation accuracy (AP50 < 50%). The framework not only classifies the lesion type but also delineates the lesion boundaries with high precision, which is critical for early detection and differential diagnosis in clinical practice. |
| format | Article |
| id | doaj-art-60f61bc92e4e498b9a8a0eb0145cde0e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-60f61bc92e4e498b9a8a0eb0145cde0e2025-08-20T03:03:33ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-03268-1CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learningAfnan Al-Ali0Ali Hamdi1Mohamed Elshrif2Keivin Isufaj3Khaled Shaban4Peter Chauvin5Sreenath Madathil6Ammar Daer7Faleh Tamimi8Raidan Ba-Hattab9Computer Science and Engineering Department, Qatar UniversityMSA UniversityQatar Computing Research Institute, HBKUQatar Computing Research Institute, HBKUComputer Science and Engineering Department, Qatar UniversityFaculty of Dental Medicine and Oral Health Sciences, McGill UniversityFaculty of Dental Medicine and Oral Health Sciences, McGill UniversityFaculty of Dental Medicine and Oral Health Sciences, McGill UniversityPre-Clinical Oral Health Sciences Department, College of Dental Medicine, QU Health, Qatar UniversityPre-Clinical Oral Health Sciences Department, College of Dental Medicine, QU Health, Qatar UniversityAbstract Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accurate differential diagnosis of 14 common oral lesions—benign, pre-malignant, and malignant—across various mouth locations using photographic images. A dataset of 2,072 clinical images was used to train and validate the model. The proposed framework integrates EfficientNet-B3 for classification and ResNet-101-based Mask R-CNN for segmentation, achieving a classification accuracy of 74.49% and segmentation performance with an average precision (AP50) of 72.18. The gradient-weighted class activation map technique was applied to the model outputs to enable visualization of the specific areas that were most influential for predictive decisions made by the model. This significantly improves upon the state-of-the-art, where previous models achieved lower segmentation accuracy (AP50 < 50%). The framework not only classifies the lesion type but also delineates the lesion boundaries with high precision, which is critical for early detection and differential diagnosis in clinical practice.https://doi.org/10.1038/s41598-025-03268-1Oral lesionOral cancerClassificationSegmentationDeep learningEarly detection |
| spellingShingle | Afnan Al-Ali Ali Hamdi Mohamed Elshrif Keivin Isufaj Khaled Shaban Peter Chauvin Sreenath Madathil Ammar Daer Faleh Tamimi Raidan Ba-Hattab CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning Scientific Reports Oral lesion Oral cancer Classification Segmentation Deep learning Early detection |
| title | CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning |
| title_full | CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning |
| title_fullStr | CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning |
| title_full_unstemmed | CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning |
| title_short | CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning |
| title_sort | claseg advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning |
| topic | Oral lesion Oral cancer Classification Segmentation Deep learning Early detection |
| url | https://doi.org/10.1038/s41598-025-03268-1 |
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