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

ABSTRACT: IntroductionOral 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.MethodsWe 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 mode

SUBMITTER: Wu M 

PROVIDER: S-EPMC12475328 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature