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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Explainable AI for Oral Cancer Diagnosis: Multiclass Classification of Histopathology Images and Grad-CAM

Jelena Štifanić1,2, Daniel Štifanić1,2, Nikola Anđelić1

  • 1Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.

Biology
|September 4, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence aids oral cancer diagnosis by analyzing histopathology images to reduce diagnostic variability. This AI approach offers a more precise and dependable method for classifying oral squamous cell carcinoma.

Keywords:
GRAD-Camartificial intelligenceexplainable deep learninghistopathology imagesoral squamous cell carcinoma

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Area of Science:

  • Oncology
  • Digital Pathology
  • Artificial Intelligence in Medicine

Background:

  • Oral cancer diagnosis relies on histological examination, which is prone to subjective interpretation and variability due to tumor heterogeneity.
  • Inter- and intra-observer variability in histopathology can impact patient treatment plans.
  • Artificial intelligence (AI) offers computational solutions to enhance diagnostic accuracy and consistency.

Purpose of the Study:

  • To develop and evaluate a two-step AI approach for automatic multiclass grading of oral squamous cell carcinoma (OSCC) using histopathology images.
  • To leverage Grad-CAM visualization for interpretability, highlighting image regions critical for AI-driven diagnosis.
  • To assist clinicians in making more precise and reliable diagnoses of OSCC.

Main Methods:

  • A two-step methodology was employed, beginning with an AI-based multiclass grading of oral histopathology images using the Xception architecture.
  • The second step involved Grad-CAM (Gradient-weighted Class Activation Mapping) for visualizing the decision-making process of the AI model.
  • Performance was quantified using Area Under the Curve (AUC) metrics, including AUC_macro and AUC_micro.

Main Results:

  • The Xception architecture demonstrated high classification performance, achieving AUC_macro of 0.929 (±σ = 0.087) and AUC_micro of 0.942 (±σ = 0.074).
  • Grad-CAM successfully provided visual explanations, pinpointing specific image areas that informed the AI's diagnostic predictions.
  • The integrated AI system showed potential for reducing diagnostic subjectivity in oral cancer assessment.

Conclusions:

  • AI algorithms, particularly the Xception architecture, show significant promise for accurate and consistent grading of oral squamous cell carcinoma from histopathology images.
  • The integration of AI with visualization techniques like Grad-CAM enhances diagnostic transparency and trust.
  • This AI-driven approach offers a more precise, dependable, and effective tool for oral cancer diagnostics, potentially improving patient outcomes.