Comparison of convolutional neural networks for classification of vocal fold nodules from high-speed video images

Abstract

Objectives

Deep learning is in this study used through convolutional neural networks (CNN) to the determination of vocal fold nodules. Through high-speed video (HSV) images and computer-assisted tools, a comparison of convolutional neural network models and their accuracy will be presented.

Methods

The data have been collected by an Ear Nose Throat (ENT) specialist with a 90° rigid scope in the years from 2007 to 2019, where 15.732 high-speed videos have been collected from 7909 patients. A total of 4000 images have been carefully selected, 2000 images were of normal vocal folds and 2000 images were of vocal folds with varying degrees of vocal fold nodules. These images were then split into training-, validation-, and testing-data set, for use with a CNN model with 5 layers (CNN5) and compared to other models: VGG19, MobileNetV2, and Inception-ResNetV2. To compare the neural network models, the following evaluation metrics have been calculated: accuracy, sensitivity, specificity, precision, and negative predictive values.

Results

All the trained CNN models have shown high accuracy when applied to the test set. The accuracy is 97.75%, 83.5%, 91.5%, and 89.75%, for CNN5, VGG19, MobileNetV2, and InceptionResNetV2, respectively.

Conclusions

Precision was identified as the most relevant performance metric for a study that focuses on the classification of vocal fold nodules. The highest performing model was MobilNetV2 with a precision of 97.7%. The average accuracy across all 4 neural networks was 90.63% showing that neural networks can be used for classifying vocal fold nodules in a clinical setting.