Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection-Reference-Cited by-同舟云学术

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Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection

Published:2024 Issue: Volume:12 Page:12189-12198
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shah Dilawar¹^ORCID,Khan Mohammad Asmat Ullah¹,Abrar Mohammad²,Amin Farhan³^ORCID,Alkhamees Bader Fahad⁴^ORCID,AlSalman Hussain⁵^ORCID

Affiliation:

1. Department of Computer Science, International Islamic University, Islamabad, Islamabad, Pakistan

2. Department of Computer Science, Bacha Khan University, Chārsadda, Pakistan

3. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea

4. Department of Information Systems, College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia

5. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10401230.pdf?arnumber=10401230

Reference21 articles.

1. CT Characterization of Aggressive Macrotrabecular-Massive Hepatocellular Carcinoma: A Step Forward to Personalized Medicine

2. BI-RADS Category 5 Assessments at Diagnostic Breast Imaging:Outcomes Analysis Based on Lesion Descriptors

3. The incidence of fatal breast cancer measures the increased effectiveness of therapy in women participating in mammography screening

4. Unsupervised representation learning with deep convolutional generative adversarial networks;Radford;arXiv:1511.06434,2015

5. Machine health surveillance system by using deep learning sparse autoencoder

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