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Related Concept Videos

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Oral squamous cell carcinoma grading classification using deep transformer encoder assisted dilated convolution with

Singaraju Ramya1, R I Minu1

  • 1Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Chennai, India.

Frontiers in Artificial Intelligence
|November 3, 2025
PubMed
Summary
This summary is machine-generated.

A new Deep Transformer Encoder-Assisted Dilated Convolution with Global Attention (DeTr-DiGAtt) model improves Oral Squamous Cell Carcinoma (OSCC) classification accuracy. This AI-driven approach enhances image analysis for better patient outcomes.

Keywords:
GAN modelGrey lag goose optimization algorithm and global attentionU-net modeladaptive bilateral filterdilated convolutional

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Oral Squamous Cell Carcinoma (OSCC) presents significant morbidity and mortality.
  • Existing OSCC classification methods like AlexNet and CNN have limitations in accuracy and data handling.
  • Accurate grading of OSCC is crucial for effective treatment planning.

Purpose of the Study:

  • To introduce a novel Deep Transformer Encoder-Assisted Dilated Convolution with Global Attention (DeTr-DiGAtt) model for enhanced OSCC classification.
  • To address limitations of current methods, including low accuracy, data scarcity, and long training times.
  • To improve the accuracy and efficiency of OSCC grading.

Main Methods:

  • Utilized a Generative Adversarial Network (GAN) for data augmentation to mitigate overfitting.
  • Employed an Adaptive Bilateral Filter (Ad-BF) for image preprocessing and noise reduction.
  • Implemented an Improved Multi-Encoder Residual Squeeze U-Net (Imp-MuRs-Unet) for precise segmentation of affected regions.
  • Applied the DeTr-DiGAtt model for OSCC grading, optimized with the Adaptive Grey Lag Goose Optimization Algorithm (Ad-GreLop).

Main Results:

  • The DeTr-DiGAtt model achieved a high accuracy (ACC) of 98.59%.
  • Demonstrated excellent performance with a Dice score of 97.97% and an Intersection over Union (IoU) of 98.08%.
  • The integrated approach significantly improved image quality, segmentation accuracy, and classification performance.

Conclusions:

  • The proposed DeTr-DiGAtt model offers a superior solution for OSCC classification compared to existing methods.
  • The combination of data augmentation, image filtering, advanced segmentation, and optimized deep learning significantly enhances diagnostic capabilities.
  • This AI-driven framework holds promise for improving the early detection and accurate grading of Oral Squamous Cell Carcinoma.