Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm-Reference-Cited by-同舟云学术

Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm

Published:2022-10-30 Issue:28 Volume:34 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Sakthivel N. K.¹^ORCID,Subasree S.²,Alias Priya M Pachhaiammal³,Tyagi Amit Kumar⁴

Affiliation:

1. Dean Nehru Institute of Engineering and Technology Tamil Nadu India

2. Professor and Head, Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore Tamil Nadu India

3. Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India

4. School of Computer Science and Engineering Vellore Institute of Technology ChennaiTamil Nadu India

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.7348

Reference24 articles.

1. ZhengS JayasumanaS Romera‐ParedesBet al.Conditional random fields as recurrent neural networks. Proceedings of the IEEE International Conference on Computer Vision;2015:1529‐1537.

2. A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

3. A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

4. Applied Optimization and Swarm Intelligence: A Systematic Review and Prospect Opportunities

5. MommiNet-v2: Mammographic multi-view mass identification networks

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