ArticleBioengineered2020
Edge detection algorithm of cancer image based on deep learning.
Article in Bioengineered, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning.Scientific reports · 2024Article
- A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.International journal of molecular sciences · 2023Article
- Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics.Frontiers in public health · 2022Article
- The interior environment design for entrepreneurship education under the virtual reality and artificial intelligence-based learning environment.Frontiers in psychology · 2022Article
- A novel classification method of lymph node metastasis in colorectal cancer.Bioengineered · 2021Article
- Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach.Biology · 2021Article
- Magnetic Resonance Imaging Image Segmentation under Edge Detection Intelligent Algorithm in Diagnosis of Surgical Wrist Joint Injuries.Contrast media & molecular imaging · 2021Article
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3 authors.
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Abstract
For the existing medical image edge detection algorithm image reconstruction accuracy is not high, the fitness of optimization coefficient is low, resulting in the detection results of low information recall, poor smoothness and low detection accuracy, we proposes an edge detection algorithm of cancer image based on deep learning. Firstly, the three-dimensional surface structure reconstruction model of cancer image was constructed. Secondly, the edge contour feature extraction method was used to extract the fine-grained features of cancer cells in the cancer image. Finally, the multi-dimensional pixel feature distributed recombination model of cancer image was constructed, and the fine-grained feature segmentation method was adopted to realize regional fusion and information recombination, and the ultra-fine particle feature was extracted. The adaptive optimization of edge detection was realized by combining with deep learning algorithm. The adaptive optimization in the process of edge detection was realized by combining with the deep learning algorithm. The experimental results show that the three-dimensional reconstruction accuracy of the proposed algorithm is about 95%, the fitness of the optimization coefficient is high, the algorithm has a strong edge information detection ability, and the output result smoothness and the accuracy of edge feature detection are high, which can effectively realize the detection of cancer image edge.
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