Min Dong

Orcid: 0000-0001-7758-7856

Affiliations:
  • Zhengzhou University, Zhengzhou, China


According to our database1, Min Dong authored at least 13 papers between 2015 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2022
A new multiscale texture surface defect detection method based on convolutional neural network.
Proceedings of the 34th IEEE International Conference on Tools with Artificial Intelligence, 2022

2019
Attention Dense-U-Net for Automatic Breast Mass Segmentation in Digital Mammogram.
IEEE Access, 2019

Self-Quotient Image based CNN: A Basic Image Processing assisting Convolutional Neural Network.
Proceedings of the 2019 3rd International Conference on Digital Signal Processing, 2019

2017
Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN.
J. Sensors, 2017

2016
A new adaptive filtering method for removing salt and pepper noise based on multilayered PCNN.
Pattern Recognit. Lett., 2016

A new method of micro-calcifications detection in digitized mammograms based on improved simplified PCNN.
Neurocomputing, 2016

A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN.
Comput. Methods Programs Biomed., 2016

An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images.
Int. J. Comput. Assist. Radiol. Surg., 2016

Nonlinear Local Transformation Based Mammographic Image Enhancement.
Proceedings of the Breast Imaging, 2016

An Effective Approach for Automatic LV Segmentation Based on GMM and ASM.
Proceedings of the Neural Information Processing - 23rd International Conference, 2016

2015
Image denoising via bivariate shrinkage function based on a new structure of dual contourlet transform.
Signal Process., 2015

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.
J. Digit. Imaging, 2015

A new study on mammographic image denoising using multiresolution techniques.
Proceedings of the Eighth International Conference on Machine Vision, 2015


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