用胶囊网络与深度置信网络自动识别口腔白斑，提升早期癌症筛查效率。
A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia
- 融合多医生标注框，构建更可靠的口腔病变数据集。
- 图像分类F1达94.23%，对象检测F1达89.34%，识别准确率高。
- 适合医疗AI研发者、口腔科医生及早筛系统开发者参考。
口腔癌是全球重大健康问题，2023年导致277,484人死亡，尤其在低收入和中等收入国家流行度最高。实现口腔黏膜病变的自动化检测有助于低成本、早期诊断。本研究收集来自全球临床专家的图像，并通过标注工具生成详尽标签，建立大规模精细标注数据集。提出一种新型方法，整合多位医生的边界框标注。结合深度置信网络（Deep Belief Network）与胶囊网络（CAPSNET），构建自动化系统以提取复杂模式。评估了两种基于深度学习的计算机视觉方法：基于CAPSNET的图像分类在检测病变图像上F1得分为94.23%，在识别需转诊图像上为93.46%；目标检测在识别需转诊病变上的F1得分为89.34%。后续分析了按转诊决策分类的性能表现。初步结果表明，深度学习具备解决该复杂问题的潜力。
原文摘要 · Abstract (English)
Oral cancer constitutes a significant global health concern, resulting in 277,484 fatalities in 2023, with the highest prevalence observed in low- and middle-income nations. Facilitating automation in the detection of possibly malignant and malignant lesions in the oral cavity could result in cost-effective and early disease diagnosis. Establishing an extensive repository of meticulously annotated oral lesions is essential. In this research photos are being collected from global clinical experts, who have been equipped with an annotation tool to generate comprehensive labelling. This research presents a novel approach for integrating bounding box annotations from various doctors. Additionally, Deep Belief Network combined with CAPSNET is employed to develop automated systems that extracted intricate patterns to address this challenging problem. This study evaluated two deep learning-based computer vision methodologies for the automated detection and classification of oral lesions to facilitate the early detection of oral cancer: image classification utilizing CAPSNET. Image classification attained an F1 score of 94.23% for detecting photos with lesions 93.46% for identifying images necessitating referral. Object detection attained an F1 score of 89.34% for identifying lesions for referral. Subsequent performances are documented about classification based on the sort of referral decision. Our preliminary findings indicate that deep learning possesses the capability to address this complex problem.
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