Narrow-band imaging (NBI) laryngoscopy is an optical-biopsy technique used for screening and diagnosing cancer of the laryngeal tract, reducing the biopsy risks but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to develop a deep-learning-based strategy for the automatic selection of informative laryngoscopic-video frames, reducing the amount of data to process for diagnosis. The strategy leans on the transfer learning process that is implemented to perform learned-features extraction using six different convolutional neural networks (CNNs) pre-trained on natural images. To test the proposed strategy, the learned features were extracted from the NBI-InfFrames dataset. Support vector machines (SVMs) and CNN-based approach were then used to classify frames as informative (I) and uninformative ones such as blurred (B), with saliva or specular reflections (S), and underexposed (U). The best-performing learned-feature set was achieved with VGG 16 resulting in a recall of I of 0.97 when classifying frames with SVMs and 0.98 with the CNN-based classification. This work presents a valuable novel approach towards the selection of informative frames in laryngoscopic videos and a demonstration of the potential of transfer learning in medical image analysis. Flowchart of the proposed approach to automatic informative-frame selection in laryngoscopic videos. The approach leans on the transfer learning process, which is implemented to perform learned-features extraction using different convolutional neural networks (CNNs) pre-trained on natural images. Frame classification is performed exploiting two different classifiers: support vector machines and fine-tuned CNNs.

中文翻译：

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通过学习的功能，将学习转移到喉镜视频中的信息帧选择中。

窄带成像（NBI）喉镜检查是一种光学活检技术，用于筛查和诊断喉癌，可降低活检风险，但会带来一些弊端，例如需要复查大量数据以进行诊断。本文的目的是开发一种基于深度学习的策略，用于自动选择信息性喉镜视频帧，从而减少要诊断的数据量。该策略依靠转移学习过程，该过程实现为使用在自然图像上预先训练的六个不同的卷积神经网络（CNN）执行学习特征提取。为了测试所提出的策略，从NBI-InfFrames数据集中提取了学习到的特征。然后使用支持向量机（SVM）和基于CNN的方法将帧分类为信息帧（I）和非信息帧，例如模糊（B），唾液或镜面反射（S）和曝光不足（U）。VGG 16实现了性能最佳的学习功能集，从而在使用SVM对帧进行分类时召回的I为0.97，而基于CNN的分类则召回了0.98。这项工作为选择喉镜视频中的信息帧提供了一种有价值的新颖方法，并展示了医学图像分析中转移学习的潜力。喉镜视频中自动信息帧选择方法的流程图。该方法依赖于转移学习过程，它被实现为使用在自然图像上预先训练的不同卷积神经网络（CNN）执行学习特征提取。利用两个不同的分类器执行帧分类：支持向量机和微调的CNN。