Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning
Open accessAbstract
The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert.
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Some reviewed mammography studies augmented extracted positive and negative patches rather than full images.
“In some works, in the literature, augmentation methods have been applied on extracted positive and negative patches instead of the whole image (Karthiga et al. 2022; Zeiser et al. 2020; Miller et al. 2022; Kim and Kim 2022; Wu et al. 2020b).”
From Medical Image Data Augmentation: Techniques, Comparisons and Interpretations (2023) · cites this paper as Zeiser et al. 2020
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Medical Image Data Augmentation: Techniques, Comparisons and Interpretations
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Diagnosis of breast cancer for modern mammography using artificial intelligence
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- Authors
- Zeiser, F. A., da Costa, C. A., Zonta, T., Marques, N. M. C., Roehe, A. V., Moreno, M., & da Rosa Righi, R.
- Affiliations
- Universidade do Vale do Rio dos Sinos (BR)
- Universidade do Oeste de Santa Catarina (BR)
- Universidade Federal de Ciências da Saúde de Porto Alegre (BR)
- Universidade Federal da Fronteira Sul (BR)
- Venue
- Journal of Digital Imaging
- Published
- 2020-03-23
- Volume
- 33
- Issue
- 4
- Pages
- 858-868
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC7522129
- OpenAlex
- W3012612365
- Cited by
- 86
- References
- 21
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 21 · 2023: 24 · 2022: 10 · 2021: 11 · 2020: 2
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (405354/2016-9)
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Keywords
- breast cancer, mammography, mass segmentation, computer-aided detection, U-Net, data augmentation, DDSM, breast density, lesion monitoring, image preprocessing
- MeSH terms
- Deep Learning, Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Humans, Mammography, Neural Networks, Computer, Early Detection of Cancer
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