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Syncretic Grad-CAM Integrated ViT-CNN Hybrids with Inherent Explainability for Early Thyroid Cancer Diagnosis from

Ahmed Y Alhafdhi1, Gibrael Abosamra1, Abdulrhman M Alshareef1

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary

This study introduces an AI framework combining convolutional neural networks and vision transformers for accurate thyroid cancer detection in ultrasound images. The model achieves high accuracy and provides interpretable results, boosting confidence in AI-assisted diagnosis.

Keywords:
ANNCNNGrad-CAMViT-EXGBoostfusion featuresthyroid cancer

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Oncology

Background:

  • Accurate thyroid cancer detection via ultrasound is challenging due to nodule heterogeneity and potential confusion with benign tissues.
  • Current deep learning models often focus on local features, limiting interpretability and clinical confidence.

Purpose of the Study:

  • To develop an integrated AI framework for enhanced thyroid ultrasound analysis.
  • To improve the accuracy and interpretability of AI-driven thyroid cancer detection.

Main Methods:

  • Proposed an integrated framework combining enhanced convolutional feature encoders (DenseNet169, VGG19) with an enhanced vision transformer (ViT-E).
  • Enabled simultaneous learning of local features and global context for feature fusion during the learning stage.
  • Utilized Gradient-weighted Class Activation Mapping (Grad-CAM) for interpretability and spatial audits for validation.

Main Results:

  • The ViT-E-DenseNet169 model achieved 98.5% accuracy, 98.9% sensitivity, and 99.15% specificity.
  • Demonstrated superior performance compared to existing hybrid models and systems.
  • Grad-CAM maps provided clinically meaningful visualizations of cancer patterns, with high accuracy (PTC up to 0.984) and low activation for benign cases (PTC = 0.002).

Conclusions:

  • The integrated ViT-E-DenseNet169 framework offers highly accurate thyroid cancer detection.
  • Provides clinically relevant interpretability via Grad-CAM spatial validation.
  • Enhances confidence in AI-assisted ultrasound diagnosis for thyroid nodules.