A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms
Open accessAbstract
Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attention mechanism. The proposed CAD system has the capability to recognize breast cancer in two scenarios: Scenario A (Binary classification) and Scenario B (Multi-classification). Data collection and preprocessing, patch image creation and splitting, and artificial intelligence-based breast lesion identification are all components of the execution framework that are applied consistently across both cases. The effectiveness of the proposed AI model is compared against three separate deep learning models: a custom CNN, the VGG16, and the ResNet50. Two datasets, CBIS-DDSM and DDSM, are utilized to construct and test the proposed CAD system. Five-fold cross validation of the test data is used to evaluate the accuracy of the performance results. The suggested hybrid CAD system achieves encouraging evaluation results, with overall accuracies of 100% and 95.80% for binary and multiclass prediction challenges, respectively. The experimental results reveal that the proposed hybrid AI model could identify benign and malignant breast tissues significantly, which is important for radiologists to recommend further investigation of abnormal mammograms and provide the optimal treatment plan.
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Riyadh et al.'s mixed deep learning CAD system achieved 100% accuracy for binary classification and 95.80% for multi-class prediction of breast lesions.
“This innovative model achieved a perfect 100% accuracy rate for binary classification and an impressive 95.80% for multi-class prediction tasks”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [97]
Riyadh et al. [97] achieved an AUC of 0.933.
“| Riyadh et al. [97] | Extracted patches centered on the points from the original X-ray | X-ray | General Electric, Siemens, and Hologic | 0.933 (AUC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [97]
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Flagged first, then by citations
Research works printed but not linked: 3
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Cited by
Sorted by citations
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
- Authors
- Al-Tam, R. M., Al-Hejri, A. M., Narangale, S. M., Samee, N. A., Mahmoud, N. F., Al-masni, M. A., & Al-antari, M. A.
- Affiliations
- Swami Ramanand Teerth Marathwada University (IN)
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Princess Nourah bint Abdulrahman University (SA)
- Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
- Corresponding author
- Narangale, Sachin M.
- ORCID
- Venue
- Biomedicines
- Publisher
- MDPI AG
- Published
- 2022-11-18
- Volume
- 10
- Issue
- 11
- Pages
- 2971
- ISSN
- 2227-9059 (ISSN-L)
- PMC
- PMC9687367
- OpenAlex
- W4309457179
- Cited by
- 74
- References
- 47
- Citations by year
- 2026: 20 · 2025: 17 · 2024: 20 · 2023: 14 · 2022: 3
- FWCI
- 4.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biomedicines (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Princess Nourah bint Abdulrahman University (PNURSP2022R206)
- 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 › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Keywords
- breast cancer classification, mammography, computer-aided diagnosis, CNN-Transformer hybrid, residual convolutional network, Transformer encoder, self-attention, breast lesion detection, benign versus malignant differentiation, CBIS-DDSM, DDSM
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