You Zhenzhen, Han Botao, Shi Zhenghao, Zhao Minghua, Du Shuangli, Yan Jing, Liu Haiqin, Hei Xinhong, Ren Xiaoyong, Yan Yan
Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.
Department of Otorhinolaryngology, Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China.
Head Neck. 2023 Dec;45(12):3129-3145. doi: 10.1002/hed.27543. Epub 2023 Oct 14.
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.
准确的声带白斑分类对于喉癌的个体化治疗和早期检测至关重要。已经提出了许多深度学习技术，但尚不清楚如何选择一种应用于喉部任务。本文介绍并可靠评估了用于声带白斑分类的现有深度学习模型。
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）、轻度发育异常（MiD）、中度发育异常（MoD）、重度发育异常（SD）和鳞状细胞癌（SCC）。使用六种经典深度学习模型AlexNet、VGG、谷歌Inception、ResNet、DenseNet和视觉Transformer进行声带白斑分类。
GoogLeNet（即谷歌Inception V1）、DenseNet-121和ResNet-152表现出出色的分类效果。白光图像分类的最高总体准确率为0.9583，而NBI图像分类的最高总体准确率为0.9478。这三个神经网络均提供了非常高的敏感性、特异性和精确值。
GoogLeNet、ResNet和DenseNet可以为声带白斑提供准确的病理分类。它有助于早期诊断，为不同程度的保守治疗或手术治疗提供判断，并减轻内镜医师的负担。
