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. 2025 Oct 17:8:1575427.
doi: 10.3389/frai.2025.1575427. eCollection 2025.

Oral squamous cell carcinoma grading classification using deep transformer encoder assisted dilated convolution with global attention

Singaraju Ramya  1 , R I Minu  1
Affiliations

Oral squamous cell carcinoma grading classification using deep transformer encoder assisted dilated convolution with global attention

Singaraju Ramya et al. Front Artif Intell. .

Abstract

In recent years, Oral Squamous Cell Carcinoma (OSCC) has been a common tumor in the orofacial region, affecting areas such as the teeth, jaw, and temporomandibular joint. OSCC is classified into three grades: "well-differentiated, moderately differentiated, and poorly differentiated," with a high morbidity and mortality rate among patients. Several existing methods, such as AlexNet, CNN, U-Net, and V-Net, have been used for OSCC classification. However, these methods face limitations, including low ACC, poor comparability, insufficient data collection, and prolonged training times. To address these limitations, we introduce a novel Deep Transformer Encoder-Assisted Dilated Convolution with Global Attention (DeTr-DiGAtt) model for OSCC classification. To enhance the dataset and mitigate over-fitting, a GAN model is employed for data augmentation. Additionally, an Adaptive Bilateral Filter (Ad-BF) is used to improve image quality and remove undesirable noise. For accurate identification of the affected region, an Improved Multi-Encoder Residual Squeeze U-Net (Imp-MuRs-Unet) model is utilized for segmentation. The DeTr-DiGAtt model is then applied to classify different OSCC grading levels. Furthermore, an Adaptive Grey Lag Goose Optimization Algorithm (Ad-GreLop) is used for hyperparameter tuning. The proposed method achieves an accuracy (ACC) of 98.59%, a Dice score of 97.97%, and an Intersection over Union (IoU) of 98.08%.

Keywords: GAN model; Grey lag goose optimization algorithm and global attention; U-net model; adaptive bilateral filter; dilated convolutional.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overall flow diagram.
Figure 2
Figure 2
Diagram for Imp-MuRs-Unet.
Figure 3
Figure 3
Architecture of VGG.
Figure 4
Figure 4
Architecture of Mobile Net.
Figure 5
Figure 5
Architecture of DeTr-DiGAtt.
ALGORITHM 1
ALGORITHM 1
Pseudocode for Ad-GreLop.
Figure 6
Figure 6
(A,B) Performance analysis of IoU and dice score.
Figure 7
Figure 7
Performance analysis of mIoU.
Figure 8
Figure 8
(A,B) Performance analysis of ACC and precision.
Figure 9
Figure 9
(A,B) Performance of recall and F1- score for proposed method.
Figure 10
Figure 10
Performance analysis of specificity.
Figure 11
Figure 11
Performance analysis of both training and testing (A) ACC and (B) loss for proposed method.
Figure 12
Figure 12
Performance analysis of confusion matrix of proposed method.
See this image and copyright information in PMC

References

    1. Abuhayi B. M., Bezabh Y. A., Ayalew A. M. (2024). Lumbar disease classification using an Involutional neural based VGG nets (INVGG). IEEE Access 12, 27518–27529. doi: 10.1109/ACCESS.2024.3367774 - DOI
    1. Ahmad M., Irfan M. A., Sadique U., Haq I. U., Jan A., Khattak M. I., et al. (2023). Multi-method analysis of histopathological image for early diagnosis of oral squamous cell carcinoma using deep learning and hybrid techniques. Cancer 15:5247. doi: 10.3390/cancers15215247, PMID: - DOI - PMC - PubMed
    1. Alanazi A. A., Khayyat M. M., Khayyat M. M., Elamin Elnaim B. M., Abdel-Khalek S. (2022). Intelligent deep learning enabled oral squamous cell carcinoma detection and classification using biomedical images. Comput. Intell. Neurosci. 2022, 1–11. doi: 10.1155/2022/7643967, PMID: - DOI - PMC - PubMed
    1. Albalawi E., Thakur A., Ramakrishna M. T., Bhatia Khan S., SankaraNarayanan S., Almarri B., et al. (2024). Oral squamous cell carcinoma detection using EfficientNet on histopathological images. Front. Med. 10:1349336. doi: 10.3389/fmed.2023.1349336, PMID: - DOI - PMC - PubMed
    1. Ananthakrishnan B., Shaik A., Kumar S., Narendran S. O., Mattu K., Kavitha M. S. (2023). Automated detection and classification of oral squamous cell carcinoma using deep neural networks. Diagnostics 13:918. doi: 10.3390/diagnostics13050918, PMID: - DOI - PMC - PubMed

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