Fuzzy Hopfield neural network with fixed weight for medical image segmentation

2002 ◽  
Vol 41 (2) ◽  
pp. 351 ◽  
Author(s):  
Chwen-Liang Chang
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Medical Image ◽  
Hopfield Neural Network ◽  
Medical Image Segmentation ◽  
Fixed Weight

A fuzzy Hopfield neural network for medical image segmentation

1996 ◽  
Vol 43 (4) ◽  
pp. 2389-2398 ◽  
Author(s):  
Jzau-Sheng Lin ◽  
Kuo-Sheng Cheng ◽  
Chi-Wu Mao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Medical Image ◽  
Hopfield Neural Network ◽  
Medical Image Segmentation

scholarly journals DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lin Teng ◽  
Hang Li ◽  
Shahid Karim
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Related Area ◽  
Segmentation Accuracy

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.

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