Deep transfer learning based algorithm for heart sound heart disease detection
Heart disease has been a major health challenge worldwide and early diagnosis and detection is crucial.The re-search aims to develop a migration learning based heart sound heart disease detection algorithm to improve the accuracy of early di-agnosis of heart diseases.The use of migration learning during training enhances the stability and generalization of the model.In ad-dition,an improved heart sound recognition model HTS-AT V2 based on the HTS-AT model is proposed for feature extraction and classification of heart sound signals.The results show that the improved algorithm achieves significant success in heart sound detec-tion,speeding up the inference and reducing the model size while there is an improvement in the detection effect.
deep learningdeep transfer learningheart sounds recognitionheart disease
万方数据
维普
