Research on Bioengineering Algorithm Based on Deep Learning Neural Network

Abstract

Deep learning (DL) is a fresh study orientation in the field of machine learning in computer science. It is recommend into machine learning to make it nearer to the customary artificial target intelligence. DL is the inherent law and express level of learning sample data, and the information get in the learning procedure is of mighty help to the explain of data such as words, images and sounds. CNN (Convolutional Neural Network) combines feature extraction with itemize process to train neural network, which has acquire mighty successful in the field of image classification. This paper focuses on the automatic classification of fetal facial ultrasound images. A 19-layer convolution network is proposed and improved. By using data enhancement, adding global mean pooling layer, reducing the number of channels in the full connection layer of the model, and optimizing learning based on parameter transfer learning of fine-tuning training, the automatic classification of fetal facial ultrasound images with limited data volume can be realized. Match with the present solutions, the depth network proposed in this paper can effectively avoid ultrasonic noise interference and learn deep features more effectively. A heavy quantity of specific analysis test have proved its effectiveness.