标题： 一种利用CapsNet和深度置信网络检测和识别口腔白细胞减少症的新方法 显示英文标题

标题： A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia

摘要： 口腔癌构成一个重要的全球健康问题，在2023年导致了277,484人死亡，高发于低收入和中等收入国家。促进口腔内可能恶性或恶性病变的自动化检测可能会带来成本效益高的早期疾病诊断。建立一个详尽标注的口腔病变广泛数据库至关重要。在这项研究中，从全球临床专家那里收集照片，他们配备了标注工具以生成全面的标签。本研究提出了一种新的方法来整合来自不同医生的边界框标注。此外，结合深度置信网络与CAPSNET的方法被用于开发提取复杂模式的自动化系统以解决这个具有挑战性的问题。本研究评估了两种基于深度学习的计算机视觉方法，用于口腔病变的自动化检测和分类，以促进口腔癌的早期检测：利用CAPSNET进行图像分类。图像分类在检测有病变的照片时达到了94.23%的F1分数，在识别需要转诊的图像时达到了93.46%的F1分数。目标检测在识别需要转诊的病变时达到了89.34%的F1分数。关于基于转诊决定类型的分类，后续性能也有所记录。我们的初步结果显示，深度学习有能力解决这个复杂的问题。 显示英文摘要

摘要： 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.