Computer-aided detection of breast Cancer on mammograms: a swarm intelligence optimized wavelet neural network approach
Open accessAbstract
Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.
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Dheeba et al used neural network to predict breast cancer, with inputs being texture information from mammographic images and outcomes being tumour indicators.
“Dheeba et al used neural network to predict breast cancer, with the inputs being the texture information from mammographic images and the outcomes being tumour indicators.35”
From Artificial intelligence in healthcare: past, present and future (2017) · cites this paper as [35]
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Artificial intelligence in healthcare: past, present and future
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- Authors
- Dheeba, J., Albert Singh, N., & Tamil Selvi, S.
- Affiliations
- Noorul Islam University (IN)
- Corresponding author
- Dheeba, J.
- ORCID
- Dheeba, J.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 49
- Pages
- 45-52
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W1978167298
- Cited by
- 380
- References
- 39
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 24 · 2023: 26 · 2022: 35 · 2021: 47 · 2020: 55 · 2019: 42 · 2018: 50 · 2017: 32 · 2016: 29 · 2015: 16 · 2014: 4 · 2013: 1
- FWCI
- 26.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- breast cancer detection, computer-aided diagnosis, mammography, particle swarm optimization, wavelet neural network, microcalcification clusters, breast mass, texture features
- MeSH terms
- Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Humans, Mammography, Neural Networks, Computer, Wavelet Analysis
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