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Related Experiment Videos

Oral Cancer Diagnosis Using an Optimized InceptionV3 Model Powered by the Aquila Metaheuristic Algorithm.

Sabura Banu Urundai Meeran1, Kavitha S1, Chinthamani B2

  • 1Department of Electrical and Electronics Engineering, Saveetha Engineering College, Saveetha Nagar, Thandalam, Chennai : 602105, Tamilnadu, India.

Asian Pacific Journal of Cancer Prevention : APJCP
|May 22, 2026
PubMed
Summary

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Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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This study enhanced oral cancer detection using an optimized deep learning model (AO-InceptionV3), achieving high accuracy in identifying lesions. The framework shows promise for real-time clinical applications, improving patient outcomes.

Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Pathology
  • Medical Image Analysis

Background:

  • Early oral cancer detection is crucial for patient survival.
  • Deep learning (DL) models offer automated medical image classification but face performance challenges.
  • Optimizing DL hyperparameters is complex and impacts diagnostic accuracy.

Purpose of the Study:

  • To introduce an enhanced diagnostic framework for oral cancer detection.
  • To combine the InceptionV3 convolutional neural network with the Aquila Optimizer (AO) for superior classification.
  • To improve the accuracy and reliability of identifying oral cancer lesions.

Main Methods:

  • A dataset of labeled oral lesion images (benign and malignant) was utilized.
  • The InceptionV3 model was fine-tuned and optimized using the Aquila Optimizer (AO).
Keywords:
Aquila OptimizerDeep LearningInception V3Oral Cancer Detection

Related Experiment Videos

  • Performance was evaluated against other DL architectures using metrics like accuracy, precision, recall, F1-score, AUC-ROC, and MCC.

    • Main Results:

    • The AO-InceptionV3 model achieved 97.80% accuracy, 97.81% precision, and 97.79% recall.
    • It demonstrated a high AUC-ROC of 99.81% and an MCC of 0.956.
    • The model exhibited robustness, reliability, and minimal inference time in distinguishing oral lesions.

    Conclusions:

    • Integrating the Aquila Optimizer with InceptionV3 significantly enhances DL model performance for oral cancer detection.
    • The proposed framework offers high accuracy, efficiency, and reliability for potential real-time clinical deployment.
    • This AI-driven system sets a benchmark for future cancer diagnostic tools.