Yanwei Wang
Purdue University
6 Papers
Yanwei Wang is an academic researcher from Purdue University. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 3, co-authored 4 publications.
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Papers
Edge detection algorithm of cancer image based on deep learning.
TL;DR: 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.
35
Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning
TL;DR: Wang et al. as mentioned in this paper proposed a fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning, which can be applied to the field of breast ultrasound diagnosis.
Three-Dimensional Reconstruction of Fuzzy Medical Images Using Quantum Algorithm
Xiaofeng Li,Jun Sui,Yanwei Wang +2 more
TL;DR: The results show that the proposed method is characterized by high matching degree of image features and balanced distribution of point clouds, and it has lower error rate than the traditional method, thereby improving the detection and recognition capability of medical images, and the algorithm has certain practical application.
Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics
TL;DR: The results demonstrate that the algorithm's segmentation effect is effective and the training loss is modest, relative overlap and accuracy all exceed 95%, and the overall segmentation performance is superior.
9
Medical Data Stream Distribution Pattern Association Rule Mining Algorithm Based on Density Estimation
Xiaofeng Li,Yanwei Wang,Dong Li +2 more
TL;DR: The experimental results show that the proposed algorithm has a contour curve closest to the true probability density curve; the dispersion degree of medical data is within a reasonable range, and the medical data has high stability; the data redundancy probability is smaller; the mining result’s RMSEA is small; data mining takes less time.