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BRIEF RESEARCH REPORT article

Front. Artif. Intell.

Sec. Pattern Recognition

Volume 8 - 2025 | doi: 10.3389/frai.2025.1575427

This article is part of the Research TopicAI-Enabled Breakthroughs in Computational Imaging and Computer VisionView all 3 articles

Oral Squamous Cell Carcinoma Grading Classification Using Deep Transformer Encoder Assisted Dilated Convolutional With Global Attention

Provisionally accepted
Singaraju  RamyaSingaraju RamyaMinu  R IMinu R I*
  • SRM Institute of Science and Technology, Chennai, India

The final, formatted version of the article will be published soon.

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, Adaptive bilateral filter, U-net model, Dilated convolutional, Grey lag goose optimization algorithm and global attention

Received: 12 Feb 2025; Accepted: 23 Sep 2025.

Copyright: © 2025 Ramya and R I. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Minu R I, r_i_minu@yahoo.co.in

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