Oral squamous cell carcinoma grading classification using deep transformer encoder assisted dilated convolution with global attention
Singaraju Ramya, R. I. Minu
TL;DR
This paper introduces a new deep learning model for classifying oral cancer grades with high accuracy using advanced techniques like transformers and data augmentation.
Contribution
The novel DeTr-DiGAtt model improves OSCC classification accuracy and efficiency using a transformer encoder and global attention mechanisms.
Findings
The proposed model achieves 98.59% accuracy in OSCC grading classification.
Segmentation results show a Dice score of 97.97% and IoU of 98.08%.
Ad-GreLop hyperparameter tuning enhances model performance.
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…
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Taxonomy
TopicsAI in cancer detection · Brain Tumor Detection and Classification · Digital Imaging for Blood Diseases