Abstract：
OBJECTIVE: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification.
METHODS: A study of a classification network based on a retrospective database.
METHODS: Academic university and hospital.
METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer.
RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network.
CONCLUSIONS: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.
METHODS: A study of a classification network based on a retrospective database.
METHODS: Academic university and hospital.
METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer.
RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network.
CONCLUSIONS: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.
摘要：
目的：准确的声带白斑分型对临床诊断和手术治疗具有指导意义。本文介绍了一个可靠的非常深的暹罗网络，用于准确的声带白斑分类。
方法：基于回顾性数据库的分类网络研究。
方法：学术大学和医院。
方法：本文使用的声带白斑的白光图像数据集分为6类：正常组织，炎性角化病，轻度发育不良,中度发育不良,严重的发育不良,和鳞状细胞癌。通过将其与6个经典的深度学习模型进行比较来评估分类性能，包括AlexNet,VGG网络，谷歌盗梦空间,ResNet,DenseNet,和视觉变压器。
结果：实验表明，与最先进的方法相比，我们提出的网络具有优越的分类性能。总体精度为0.9756。灵敏度和特异性的值也非常高。混淆矩阵为6类分类任务提供了信息，并证明了我们提出的网络的优越性。
结论：我们非常深入的暹罗网络可以提供声带白斑的准确分类结果，这有助于早期检测，临床诊断,和手术治疗。在白光图像中获得的优异性能可以降低患者的成本，尤其是那些生活在发展中国家的人。
方法：基于回顾性数据库的分类网络研究。
方法：学术大学和医院。
方法：本文使用的声带白斑的白光图像数据集分为6类：正常组织，炎性角化病，轻度发育不良,中度发育不良,严重的发育不良,和鳞状细胞癌。通过将其与6个经典的深度学习模型进行比较来评估分类性能，包括AlexNet,VGG网络，谷歌盗梦空间,ResNet,DenseNet,和视觉变压器。
结果：实验表明，与最先进的方法相比，我们提出的网络具有优越的分类性能。总体精度为0.9756。灵敏度和特异性的值也非常高。混淆矩阵为6类分类任务提供了信息，并证明了我们提出的网络的优越性。
结论：我们非常深入的暹罗网络可以提供声带白斑的准确分类结果，这有助于早期检测，临床诊断,和手术治疗。在白光图像中获得的优异性能可以降低患者的成本，尤其是那些生活在发展中国家的人。
