Abstract：
Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification.
We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer.
GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values.
GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.
We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer.
GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values.
GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.
摘要：
背景：准确的声带白斑分类对于喉癌的个体化治疗和早期发现至关重要。已经提出了许多深度学习技术，但目前还不清楚如何选择一个应用于喉部任务。本文介绍并可靠地评估了用于声带白斑分类的现有深度学习模型。
方法：我们创建了声带白斑的白光和窄带成像（NBI）图像数据集，将其分为六类：正常组织（NT），炎性角化病(IK),轻度发育不良（MiD），中度发育不良（MoD），重度发育不良（SD），鳞状细胞癌（SCC）。使用六个经典的深度学习模型进行声带白斑分类，AlexNet,VGG,谷歌盗梦空间,ResNet,DenseNet,和视觉变压器。
结果：GoogLeNet(即，谷歌盗梦空间V1)，DenseNet-121和ResNet-152执行出色的分类。白光图像分类的总体精度最高为0.9583，而NBI图像分类的总体精度最高为0.9478。这三个神经网络都提供了非常高的灵敏度，特异性，和精度值。
结论：GoogLeNet，ResNet,DenseNet可以提供声带白斑的准确病理分类。它有助于早期诊断，提供不同程度的保守治疗或手术治疗的判断，减轻内窥镜医师的负担。
方法：我们创建了声带白斑的白光和窄带成像（NBI）图像数据集，将其分为六类：正常组织（NT），炎性角化病(IK),轻度发育不良（MiD），中度发育不良（MoD），重度发育不良（SD），鳞状细胞癌（SCC）。使用六个经典的深度学习模型进行声带白斑分类，AlexNet,VGG,谷歌盗梦空间,ResNet,DenseNet,和视觉变压器。
结果：GoogLeNet(即，谷歌盗梦空间V1)，DenseNet-121和ResNet-152执行出色的分类。白光图像分类的总体精度最高为0.9583，而NBI图像分类的总体精度最高为0.9478。这三个神经网络都提供了非常高的灵敏度，特异性，和精度值。
结论：GoogLeNet，ResNet,DenseNet可以提供声带白斑的准确病理分类。它有助于早期诊断，提供不同程度的保守治疗或手术治疗的判断，减轻内窥镜医师的负担。
