Abstract
With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people's time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.
Highlights
Medical image enhances the sharpness of the original image by denoising, restoring, etc. or highlights certain information in the image for the segmentation of organs and tissues of interest for quantitative analysis [1,2,3]
It can be seen from this that the traditional CNN segmentation method is less effective than the proposed method, the CNN method can achieve the accuracy of Zhao’s proposed algorithm, which fully demonstrates the great advantages of deep learning theory in medical image segmentation
Inspired by the visual attention mechanism in the human visual system, this paper presents the problem definition of the feedback adjustment mechanism in the deep convolutional neural network with object classification as the task
Summary
Medical image enhances the sharpness of the original image by denoising, restoring, etc. or highlights certain information in the image for the segmentation of organs and tissues of interest for quantitative analysis [1,2,3]. (4) Medical image segmentation based on deep learning: e convolutional neural network (CNN) was proposed by Lecun et al It was the first true multilayer structure-learning algorithm that could reduce the number of parameters by using spatial relative relations to improve the training performance [17,18,19]. Relevant scholars have proposed a number of convolutional neural networks with feedback mechanisms and applied the network to image classification and image recognition [35,36,37,38] These practical applications have achieved certain effects (such as improved accuracy), there are still problems such as unclear feedback mechanism, optimization of feedback mechanism, and computational efficiency.
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