口腔鳞状细胞癌（OSCC）是全球第八大最常见的癌症，导致口腔层和膜内结构完整性的丧失。尽管其患病率很高，但早期诊断对于有效治疗至关重要。本研究旨在利用深度学习在医学图像分类方面的最新进展，自动实现口腔组织病理学图像的早期诊断，从而促进口腔癌的及时、准确检测。深度学习卷积神经网络（CNN）模型对良性和恶性口腔活检组织病理学图像进行分类。通过利用 17 个预训练的 DL-CNN 模型，两步统计分析确定预训练的 EfficientNetB0 模型最为优越。通过将双重注意力网络 (DAN) 纳入模型架构中，实现了 EfficientNetB0 的进一步增强。改进后的 EfficientNetB0 模型表现出了令人印象深刻的性能指标，包括 91.1% 的准确率、92.2% 的灵敏度、91.0% 的特异性、91.3% 的精度，假阳性率（FPR）为1.12%，F1得分为92.3%，Matthews相关系数（MCC）为90.1%，kappa为88.8%，计算时间为66.41%。值得注意的是，该模型超越了该领域最先进方法的性能。将深度学习技术，特别是增强型 EfficientNetB0 模型与 DAN 相结合，通过口腔组织病理学图像分析对口腔癌进行自动化早期诊断显示出有希望的结果。这一进展对于提高口腔癌治疗策略的疗效具有巨大潜力。© 2024。作者。

Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment.This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer.A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture.The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field.Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.© 2024. The Author(s).