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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| Published in: | Applied Computational Intelligence and Soft Computing Vol. 2024; no. 1 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Hindawi
2024
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1687-9724, 1687-9732 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Ullah Khan, Mohammad Asmat Abrar, Mohammad Shah, Dilawar |
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| Cites_doi | 10.1117/12.2543506 10.1007/s00330-022-08617-6 10.3390/jimaging8050141 10.21203/rs.3.rs-2851632/v1 10.1002/acs.2916 10.1186/s12885-023-10890-7 10.1109/ASET56582.2023.10180771 10.3322/caac.21660 10.1016/j.isatra.2019.08.032 10.1109/tsmc.2022.3186610 10.1016/j.cmpb.2021.106019 10.1016/j.eswa.2020.113968 10.1109/tmi.2021.3108949 10.1016/j.asoc.2022.108836 10.1109/CICN49253.2020.9242551 10.1007/s00500-016-2447-9 10.1016/j.compbiomed.2021.104248 10.1007/s00500-023-08061-8 10.1038/s41598-023-29521-z |
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| Copyright | Copyright © 2024 Dilawar Shah et al. COPYRIGHT 2024 John Wiley & Sons, Inc. Copyright © 2024 Dilawar Shah et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | 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 |
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| Title | Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment |
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