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Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion
Open accessAbstract
After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.
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“The implementation of this framework on the Augmented Breast Ultrasound Images (BUSI) dataset resulted in an impressive highest accuracy of 99.1%”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [100]
In the citing paper
“| Jabeen et al. [100] | Original ultrasound images data | Ultrasound Images | BUSI dataset | 99.1% (ACC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [100]
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Metadata
- Authors
- Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y.-D., Hamza, A., Mickus, A., & Damaševičius, R.
- Affiliations
- HITEC University (PK)
- University of Ha'il (SA)
- Prince Sattam Bin Abdulaziz University (SA)
- Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
- Vytautas Magnus University (LT)
- Corresponding author
- Damaševičius, Robertas
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-01-21
- Volume
- 22
- Issue
- 3
- Pages
- 807
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8840464
- OpenAlex
- W4207017358
- Cited by
- 314
- References
- 65
- Citations by year
- 2026: 47 · 2025: 66 · 2024: 76 · 2023: 82 · 2022: 43
- FWCI
- 21.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Vytautas Magnus University (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- breast cancer classification, breast ultrasound images, feature fusion, feature selection, transfer learning, Darknet-53, data augmentation, BUSI dataset, early cancer detection
- MeSH terms
- Deep Learning, Breast, Breast Neoplasms, Female, Humans, Probability, Ultrasonography, Mammary
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