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A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral

Bharanidharan Nagarajan1, Sannasi Chakravarthy2, Vinoth Kumar Venkatesan1

  • 1School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India.

Diagnostics (Basel, Switzerland)
|November 24, 2023
PubMed
Summary

Early detection of oral cancer improves survival. This study introduces a deep learning model using the Modified Gorilla Troops Optimizer to enhance oral squamous cell carcinoma histopathology image classification, achieving 95% accuracy.

Keywords:
CNNGorilla Troops Optimizerdeep learning frameworkhistopathologic imagesoral cancerswarm intelligence

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Oral squamous cell carcinoma (OSCC) is a prevalent cancer where early detection is crucial for reducing mortality.
  • Automated analysis of histopathology images offers a promising avenue for identifying abnormal oral lesions.
  • Current deep learning models require optimization for accurate classification of these images.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for classifying oral histopathology images as normal or OSCC.
  • To investigate the efficacy of a novel swarm intelligence technique, the Modified Gorilla Troops Optimizer (MGTO), as an intermediate layer in a deep learning model.
  • To improve the classification accuracy of OSCC detection using optimized features.

Main Methods:

  • A deep learning framework was designed incorporating feature extraction, an intermediate optimization layer, and classification layers.
  • Three Convolutional Neural Network (CNN) architectures (InceptionV2, MobileNetV3, EfficientNetB3) were used for feature extraction.
  • The Modified Gorilla Troops Optimizer was implemented as an intermediate layer to refine extracted features for classification.

Main Results:

  • MobileNetV3 achieved the highest accuracy of 0.89 among the tested CNN architectures for feature extraction.
  • Integrating the Modified Gorilla Troops Optimizer as an intermediate layer significantly improved classification accuracy to 0.95.
  • The study utilized three datasets totaling 2784 normal and 3632 OSCC subjects.

Conclusions:

  • The proposed deep learning framework, enhanced by the Modified Gorilla Troops Optimizer, demonstrates superior performance in classifying oral squamous cell carcinoma histopathology images.
  • The MGTO effectively transforms extracted features, making them more suitable for accurate classification, thereby aiding in early cancer detection.
  • This approach holds significant potential for clinical application in the automated diagnosis of oral cancer, improving diagnostic efficiency and patient outcomes.