With the development of computer vision and image segmentation technology, medical image segmentation and recognition\ntechnology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on\nartificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable\nenergy resources and peopleâ??s time but also requires certain expertise to obtain useful feature information, which no\nlonger meets the practical application requirements of medical image segmentation and recognition. As an efficient image\nsegmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical\nimage segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid\ndevelopment of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an\neffective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is\npresented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information\nrecovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural\nnetwork is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image\nclassifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and\nmorphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation\nmethod has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various\nmedical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new\nattempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and\nmethods for further development and improvement of adaptive medical image segmentation technology.
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