Proposing a Framework to Analyze Breast Cancer in Mammogram Images Using Global Thresholding, Gray Level Co‐Occurrence Matrix, and Convolutional Neural Network (CNN)

Author:

Dixit Tanishka,Singh Namrata

Publisher

Wiley

Reference40 articles.

1. Moustapha MohamedSaleck Abdelmajid ELMoutaouakkil MohamedRmili;Hybrid Clustering and Texture Features in Segmentation of Breast Masses in Mammograms; 978‐1‐5386‐7266‐2/18/$31.00 ©2018 IEEE.

2. ShafiullahSoomro Kwang NamChoi;Robust Active Contours For Mammogram Image Segmentation; 978‐1‐5090‐2175‐8/17/$31.00 ©2017 IEEE ICIP 2017.

3. Syeda IffatNaz MonikaShah Mohammed Imamul HassanBhuiyan;Automatic Segmentation of Pectoral Muscle in Mammogram Images Using Global Thresholding and Weak Boundary Approximation; 2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON‐ECE) 18‐19 December 2017 WIT Dehradun India.

4. AyushShrivastava ArpitChaudhary DevangKulshreshtha Vibhav PrakashSingh RajeevSrivastava;Automated Digital Mammogram Segmentation Using Dispersed Region Growing and Sliding Window Algorithm; 2017 2nd International Conference on Image Vision and Computing.

5. WentaoZhu XiangXiang Trac D.Tran Gregory D.Hager XiaohuiXie;ADVERSARIAL DEEP STRUCTURED NETS FOR MASS SEGMENTATION FROM MAMMOGRAMS; 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) April 4‐7 2018 Washington D.C. USA.

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