Abstract
For the small-scale motion in medical motion images, the traditional medical motion image intelligent recognition algorithm has low recognition accuracy, and requires a large amount of calculation statistics. There is no self-learning function, which seriously affects the accuracy and speed of medical motion image recognition. Therefore, in order to improve the accuracy of human body small-scale motion recognition in medical motion images and the computational efficiency of large-scale data sets, an intelligent recognition algorithm based on convolutional neural network for medical motion images is proposed. The algorithm first learns the dense trajectory features and depth features, and then further fuses the dense trajectory features with the deep learning features. Finally, the extreme learning machine is applied to the convolutional neural network, and the fused features are further trained as input information of the convolutional neural network, and the features from the bottom layer to the upper layer can be extracted step by step from the raw data of the pixel level. Simulation experiments show that the algorithm can effectively improve the recognition accuracy of small-scale motion in medical moving images and improve the speed of motion.
Highlights
The intelligent recognition of medical motion images is a cross-disciplinary field of comprehensive medical imaging, mathematical modeling, and computer technology [1], [2]
For the intelligent recognition of small-scale motion in medical motion images, the existing convolutional neural network model cannot analyze the relationship between the parts of the image and the nuances in the image, so it cannot describe the relationship between the small-scale motion and the whole image
The existing convolutional neural network model has low robustness and poor processing effect on high-dimensional image data, which limits the versatility and adaptability of the model and is difficult to implement in clinical applications
Summary
The intelligent recognition of medical motion images is a cross-disciplinary field of comprehensive medical imaging, mathematical modeling, and computer technology [1], [2]. With the development of convolutional neural networks, convolutional neural networks have become the preferred method to solve the task of extracting high-dimensional features It can extract deep and abstract features in the data, and can capture long-range dependencies in the data in an efficient way, and can effectively analyze and identify medical moving images [13], [14]. In literature [20], feature extraction is performed by convolutional neural network, and generalized estimation equations are used to evaluate the recognition accuracy of medical moving images. In order to satisfy the intelligent recognition of highdimensional data and small-scale motion at the same time, this paper designs an intelligent recognition algorithm based on convolutional neural network for medical motion images. The technical contributions of our paper can be concluded as follows: First: This paper presents an intelligent recognition algorithm for medical motion images based on convolutional neural networks. This puts higher requirements on the feature extraction of medical motion images
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