Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment

Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convo...

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Bibliographic Details
Published in:	Applied Computational Intelligence and Soft Computing Vol. 2024; no. 1
Main Authors:	Shah, Dilawar, Ullah Khan, Mohammad Asmat, Abrar, Mohammad
Format:	Journal Article
Language:	English
Published:	New York Hindawi 2024 John Wiley & Sons, Inc Wiley
Subjects:	Accuracy Algorithms Artificial intelligence Breast cancer Cancer Clustering Data analysis Data points Datasets Deep learning Diagnosis Effectiveness Evaluation Generative adversarial networks Image databases Machine learning Mammography Medical imaging Medical imaging equipment Medical research Neural networks Outliers (statistics) R&D Research & development Similarity Synthetic data United Kingdom > UK Pakistan
ISSN:	1687-9724, 1687-9732
Online Access:	Get full text
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