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Related Experiment Video

Updated: Jun 12, 2025

Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma
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Enhancing Oral Squamous Cell Carcinoma Detection Using Histopathological Images: A Deep Feature Fusion and Improved

Amad Zafar1, Majdi Khalid2, Majed Farrash2

  • 1Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea.

Bioengineering (Basel, Switzerland)
|September 27, 2024
PubMed
Summary

This study introduces an AI-driven method for early oral cancer detection using histopathological images. The approach achieves 97.78% accuracy, offering a promising tool for clinical diagnosis.

Keywords:
machine learningmouth canceroral canceroral squamous cell carcinoma

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Oral cancer, or oral squamous cell carcinoma (OSCC), is a significant global health concern, with early detection crucial for improving patient survival rates.
  • Current diagnostic methods for OSCC can be time-consuming and may benefit from automated, objective analysis.
  • Machine learning and image processing offer potential for enhancing the accuracy and efficiency of OSCC detection from histopathological images.

Purpose of the Study:

  • To develop and evaluate an automated machine learning approach for the detection of oral squamous cell carcinoma (OSCC) using histopathological images.
  • To investigate the efficacy of deep feature extraction, feature fusion, and optimized feature selection for improving OSCC classification performance.
  • To assess the clinical applicability of the proposed framework as an aid for medical professionals in diagnosing OSCC.

Main Methods:

  • Deep features were extracted from histopathological images using pretrained models (ResNet-101, EfficientNet-b0).
  • Feature fusion was performed using the canonical correlation approach, followed by feature selection using the binary-improved Haris Hawks optimization (b-IHHO) algorithm.
  • The optimized features were used to train a k-nearest neighbors (kNN) classifier.

Main Results:

  • The proposed framework achieved a high OSCC classification rate of 97.78%.
  • The b-IHHO algorithm effectively reduced feature vector size to an average of 899 while enhancing classification performance.
  • Statistical analysis confirmed the stability, reliability, and significance (p < 0.01) of the b-IHHO approach compared to other feature selection methods and state-of-the-art techniques.

Conclusions:

  • The developed image-processing-based machine learning approach demonstrates high accuracy and robustness for OSCC detection.
  • The combination of deep feature extraction, fusion, and b-IHHO optimization offers a powerful tool for enhancing diagnostic performance.
  • The proposed framework shows significant potential for clinical implementation to assist healthcare providers in the early and accurate diagnosis of oral cancer.