An Advanced Ensemble of Deep Learning Models for Breast Cancer Segmentation and Classification with Two-Tier Optimization Algorithms
Received: 11 June 2025 | Revised: 11 July 2025 | Accepted: 16 July 2025 | Online: 8 August 2025
Corresponding author: M. Sreevani
Abstract
Breast Cancer (BC) is one of the most common cancers among women. Routine mammography is substantial because asymptomatic BC does not show early signs, making early detection difficult. Automated methods, including Deep Learning (DL) models, have gained significant attention for analyzing mammographic images and enhancing diagnostic accuracy. Successful AI training for these medical tasks depends on large datasets with accurately annotated lesion locations. This study proposes an Advanced Ensemble Deep Learning Model for Breast Cancer Segmentation and Classification with a Two-Tier Optimization (AEDL-BCSCT2O) approach to segment and classify BC using advanced DL and optimization techniques. The model initially applies Adaptive Bilateral Filtering (ABF) for noise removal and CLAHE for contrast enhancement to improve image quality. The DeepLabV3+ segmentation method is enhanced through parameter optimization using the Lemur Optimizer (LO). The NASNetMobile model is utilized for feature extraction. An ensemble of Deep Belief Network (DBN), Graph Convolutional Network (GCN), and Sparse Stacked Autoencoder (SSAE) models is used for improved classification. Finally, the Osprey Optimization Algorithm (OOA) approach is utilized for tuning. The validation results show that the AEDL-BCSCT2O method achieves 99.76% accuracy, outperforming existing models.
Keywords:
ensemble deep learning, breast cancer, two-tier optimization, feature extraction, image preprocessingDownloads
References
S. Sakib, N. Yasmin, A. K. Tanzeem, F. Shorna, K. Md. Hasib, and S. B. Alam, "Breast Cancer Detection and Classification: A Comparative Analysis Using Machine Learning Algorithms," in Proceedings of Third International Conference on Communication, Computing and Electronics Systems, 2022, pp. 703–717.
X. Wang et al., "Intelligent Hybrid Deep Learning Model for Breast Cancer Detection," Electronics, vol. 11, no. 17, Jan. 2022, Art. no. 2767.
G. Hamed, M. A. E. R. Marey, S. E. S. Amin, and M. F. Tolba, "Deep Learning in Breast Cancer Detection and Classification," in Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)12682, 2020, pp. 322–333.
N. M. ud din, R. A. Dar, M. Rasool, and A. Assad, "Breast cancer detection using deep learning: Datasets, methods, and challenges ahead," Computers in Biology and Medicine, vol. 149, Oct. 2022, Art. no. 106073.
M. Kumar, S. Singhal, S. Shekhar, B. Sharma, and G. Srivastava, "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning," Sustainability, vol. 14, no. 21, Jan. 2022, Art. no. 13998.
B. S. Abunasser, M. R. J. AL-Hiealy, I. S. Zaqout, and S. S. Abu-Naser, "Breast cancer detection and classification using deep learning Xception algorithm," International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, 2022.
A. Saber, M. Sakr, O. M. Abo-Seida, A. Keshk, and H. Chen, "A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique," IEEE Access, vol. 9, pp. 71194–71209, 2021.
T. N. Nguyen, T. T. Nguyen, T. H. Nguyen, and B. V. Ngo, "A Robust Approach for Breast Cancer Classification from DICOM Images," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23499–23505, Jun. 2025.
A. Naz, H. Khan, I. Ud Din, A. Ali, and M. Husain, "An Efficient Optimization System for Early Breast Cancer Diagnosis based on Internet of Medical Things and Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15957–15962, Aug. 2024.
O. I. Ramadan et al., "Enhancing Breast Cancer Classification based on BPSO Feature Selection and Machine Learning Techniques," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23907–23916, Jun. 2025.
M. R. Islam et al., "Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images," Machine Learning with Applications, vol. 16, Jun. 2024, Art. no. 100555.
E. D. Carvalho, O. P. da Silva Neto, and A. O. de Carvalho Filho, "Deep learning-based tumor segmentation and classification in breast MRI with 3TP method," Biomedical Signal Processing and Control, vol. 93, Jul. 2024, Art. no. 106199.
T. Huang, H. Yin, and X. Huang, "Deep learning and multiscale analysis for epithelial-mesenchyme segmentation and classification in breast cancer histological images," Signal, Image and Video Processing, vol. 18, no. 11, pp. 7741–7754, Nov. 2024.
M. Arfi, S. C. Yadav, and S. L. Tripathi, "An integrated computer-aided diagnosis BCanD model for detection, segmentation and classification of breast cancer," Engineering Research Express, vol. 6, no. 3, Jun. 2024, Art. no. 035240.
D. Shah, M. A. U. Khan, M. Abrar, and M. Tahir, "Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach," International Journal of Intelligent Systems, vol. 2024, no. 1, 2024, Art. no. 5564649.
F. Gurcan, "Enhancing breast cancer prediction through stacking ensemble and deep learning integration," PeerJ Computer Science, vol. 11, Feb. 2025, Art. no. e2461.
S. Lee, Y. Zhao, and W. Choi, "High-rate emphasized DeepLabV3Plus for Semantic Segmentation of Breast Cancer-related Hematoxylin and Eosin-stained Images," in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, Jul. 2024, pp. 1–4.
M. Kaddes, Y. M. Ayid, A. M. Elshewey, and Y. Fouad, "Breast cancer classification based on hybrid CNN with LSTM model," Scientific Reports, vol. 15, no. 1, Feb. 2025, Art. no. 4409.
A. Saber, S. Elbedwehy, W. A. Awad, and E. Hassan, "An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors," Neural Computing and Applications, vol. 37, no. 6, pp. 4881–4894, Feb. 2025.
"CBIS-DDSM: Breast Cancer Image Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset.
S. Mudrakola and N. Hegde, "Removal of noise on mammogram breast images using filtering methods," Concurrency and Computation: Practice and Experience, vol. 35, no. 1, 2023, Art. no. e7444.
Z. Jiang et al., "Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration," Cancers, vol. 16, no. 2, Jan. 2024, Art. no. 322.
A. A. Abd El-Aziz, M. A. Mahmood, and S. Abd El-Ghany, "Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images," Diagnostics, vol. 14, no. 20, Jan. 2024, Art. no. 2274.
M. A. Al-Betar, A. K. Abasi, Z. A. A. Alyasseri, S. Fraihat, and R. F. Mohammed, "A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer," Algorithms, vol. 17, no. 4, Apr. 2024, Art. no. 160.
G. Altan, "Breast cancer diagnosis using deep belief networks on ROI images," Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 2, pp. 286–291, Apr. 2022.
S. Palmal, N. Arya, S. Saha, and S. Tripathy, "Breast cancer survival prognosis using the graph convolutional network with Choquet fuzzy integral," Scientific Reports, vol. 13, no. 1, Sep. 2023, Art. no. 14757.
V. J. Kadam, S. M. Jadhav, and K. Vijayakumar, "Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression," Journal of Medical Systems, vol. 43, no. 8, Jul. 2019, Art. no. 263.
D. Zheng, Y. Zhang, X. Guo, Y. Ning, and R. Wei, "Research on the remaining useful life prediction method for lithium-ion batteries based on feature engineering and CNN-BiGRU-AM model," Ionics, vol. 31, no. 6, pp. 5717–5736, Jun. 2025.
V. Jaiswal, P. Saurabh, U. K. Lilhore, M. Pathak, S. Simaiya, and S. Dalal, "A breast cancer risk predication and classification model with ensemble learning and big data fusion," Decision Analytics Journal, vol. 8, Sep. 2023, Art. no. 100298.
R. Qasrawi et al., "Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications," Heliyon, vol. 10, no. 19, Oct. 2024.
Z. Liu, J. Peng, X. Guo, S. Chen, and L. Liu, "Breast cancer classification method based on improved VGG16 using mammography images," Journal of Radiation Research and Applied Sciences, vol. 17, no. 2, Jun. 2024, Art. no. 100885.
S. Arooj et al., "Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification," Computers, Materials & Continua, vol. 77, no. 3, pp. 2813–2831, 2023.
Downloads
How to Cite
License
Copyright (c) 2025 M. Sreevani, R. Latha
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
