目的:口腔鳞状细胞癌(OSCC)是全球第八大流行癌症,导致口腔层和膜内结构完整性的丧失。尽管流行率很高,早期诊断是有效治疗的关键。
目的:本研究旨在利用深度学习的最新进展进行医学图像分类,以自动化口腔组织病理学图像的早期诊断。从而促进口腔癌的及时和准确检测。
方法: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模型是最优越的。EfficientNetB0的进一步增强是通过将双注意力网络(DAN)纳入模型架构来实现的。
结果:改进的EfficientNetB0模型展示了令人印象深刻的性能指标,包括91.1%的准确率,灵敏度为92.2%,特异性91.0%,精度为91.3%,假阳性率(FPR)为1.12%,F1得分为92.3%,马修斯相关系数(MCC)为90.1%,卡帕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。
结论:集成深度学习技术,特别是带有DAN的增强型EfficientNetB0模型,显示了通过口腔组织病理学图像分析自动早期诊断口腔癌的有希望的结果。这一进步对于提高口腔癌治疗策略的疗效具有显著的潜力。
OBJECTIVE: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.