Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also...

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Vydané v:Computational intelligence and neuroscience Ročník 2022; s. 1 - 12
Hlavní autori: Li, Qun, Liu, Linlin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Hindawi 13.06.2022
John Wiley & Sons, Inc
Predmet:
Active learning
Algorithms
Artificial Intelligence
Blood vessels
Clustering
Consciousness
Cutting
Data processing
Diabetes mellitus
Genetic algorithms
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Machine learning
Mathematical optimization
Medical imaging
Medical imaging equipment
Neural networks
Neural Networks, Computer
Optimization
Organs
Retina
Sensitivity
Wavelet Analysis
Wavelet transforms
ISSN:1687-5265, 1687-5273, 1687-5273
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Shrnutí:In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.
Bibliografia:ObjectType-Article-1
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Academic Editor: Kapil Sharma
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/3500592