Introduction

Oral squamous cell carcinoma (OSCC) is a prevalent and aggressive malignant tumor in the head and neck, known for its high metastatic rate and recurrence, posing serious threats to patients’ lives. Relying solely on pathologists for diagnosis is time-consuming, labor-intensive, and prone to subjective bias. Therefore, developing artificial intelligence methods for automated detection is of significant clinical value and urgently needed.

Methods

We have developed a novel deep learning model based on an improved lightweight EfficientNetSwift, a lightweight deep learning framework designed to achieve precise and automated detection of pathological images of oral squamous cell carcinoma (OSCC). By comparing our model with mainstream models such as ResNet, MobileNet, and VIT, our model achieved superior performance in terms of precision, accuracy, and other metrics.

Results

In this study, EfficientNetSwift achieved the best results for detecting OSCC from pathological images, with 95.3% accuracy and an AUC of 0.99 using 20,180,050 parameters. This is half of ResNet's parameters and significantly fewer than VGG's, while only slightly more than MobileNet's but with better performance. The Swin Transformer performed the worst.

Conclusion

The automatic detection of OSCC using deep learning can significantly reduce labor costs and decrease the workload of clinicians. Additionally, it can assist doctors in diagnosing the disease more efficiently and accurately, providing precise prognostic predictions. This lays a solid foundation for the formulation of personalized treatment plans.

Search results

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