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  1. Home
  3. Efficientnetswift: A Lightweight And Precise Deep Learning Model For Detecting Oral Squamous Cell Carcinoma Using Pathological Images.
  1. Home
  3. Efficientnetswift: A Lightweight And Precise Deep Learning Model For Detecting Oral Squamous Cell Carcinoma Using Pathological Images.

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EfficientNetSwift: A Lightweight and Precise Deep Learning Model for Detecting Oral Squamous Cell Carcinoma Using

Min Wu1, Yue Hu2, Fa Tian3

  • 1Department of Stomatology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Technology in Cancer Research & Treatment
|September 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new deep learning model, EfficientNetSwift, accurately detects oral squamous cell carcinoma (OSCC) from pathological images. This AI approach offers a faster, more reliable alternative to manual diagnosis, improving patient outcomes.

Keywords:
EfficientNetSwiftaided automated diagnosislightweight deep learningoral squamous cell carcinomapathological image detection

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Oral squamous cell carcinoma (OSCC) is an aggressive head and neck cancer with high recurrence and metastasis rates.
  • Current diagnostic methods relying on pathologists are time-consuming, labor-intensive, and prone to subjective bias.
  • Automated detection of OSCC using artificial intelligence (AI) is crucial for improving clinical efficiency and accuracy.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for the precise and automated detection of OSCC from pathological images.
  • To compare the performance of the developed model against mainstream deep learning architectures.

Main Methods:

  • A novel deep learning model, EfficientNetSwift, was developed for automated OSCC detection.
  • The model's performance was evaluated and compared with ResNet, MobileNet, and Vision Transformer (VIT) models.
  • Key performance metrics included accuracy, precision, and Area Under the Curve (AUC).

    • Main Results:

    • EfficientNetSwift achieved 95.3% accuracy and an AUC of 0.99 in detecting OSCC from pathological images.
    • The model demonstrated superior performance compared to ResNet, MobileNet, and VIT, with significantly fewer parameters than ResNet and VGG.
    • Swin Transformer exhibited the poorest performance among the evaluated models.

    Conclusions:

    • Deep learning-based automated detection of OSCC can significantly reduce clinician workload and labor costs.
    • The developed AI model assists in more efficient and accurate diagnosis and prognostic prediction for OSCC.
    • This technology provides a strong foundation for personalized treatment planning in oral cancer care.