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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Veröffentlicht in:Applied Computational Intelligence and Soft Computing Jg. 2024; H. 1
Hauptverfasser: Shah, Dilawar, Ullah Khan, Mohammad Asmat, Abrar, Mohammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 2024
John Wiley & Sons, Inc
Wiley
Schlagworte:
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
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Zusammenfassung: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 convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1687-9724
1687-9732
DOI:10.1155/2024/1122109