Comparative Study on Diagnosis and Recognition Methods of Vocal Cord Diseases Based on Deep Learning
In the field of medical image diagnosis,computer-aided diagnostic technology has improved the accuracy of image diagnosis,but laryngoscope image Deep Learning models for vocal cord disease are still relatively scarce,which to some extent limits the development of the field of vocal cord disease recognition.This paper uses the classic VGG-Net algorithm and an algorithm that introduces Attention Mechanism to classify laryngoscope images.Evaluate the performance of these two algorithms in medical image classification by comparing their accuracy,recall/sensitivity,and specificity.The experimental results show that the SA,SE-Net,CBAM,and ECA-Net algorithms that introduce Attention Mechanisms have significantly better performance than the VGG-Net algorithm.The combination of Deep Learning and Attention Mechanisms can significantly improve the accuracy and efficiency of laryngoscopy image diagnosis for vocal cord disease,which is of great significance for the healthy development of the future medical industry.
medical image diagnosisvocal cord diseaselaryngoscope imageVGG-Net algorithmAttention Mechanism
国家科技期刊平台
NSTL
万方数据
维普
