Breast cancer is one of the most prevalent and serious public health challenges worldwide, being the leading cause of cancer-related deaths among women. Early detection is a critical factor in improving survival rates, as it allows for timely intervention and treatment. The complexity of diagnosing breast cancer from histopathology images has led to the development of advanced techniques using artificial intelligence (AI) and machine learning. This study introduces a novel deep learning ensemble approach to classify breast cancer histopathology images using publicly available datasets. The primary objective of this research is to improve the classification accuracy of breast cancer images by leveraging a combination of two deep learning models. The proposed approach utilizes the VGG16 and VGG19 models, which were both fine-tuned to enhance their performance. The results demonstrate that the ensemble method, which averages the predicted probabilities from both models, leads to a more robust classifier. Specifically, the fine-tuning of the VGG16 and VGG19 models contributes significantly to improving the model’s performance. The ensemble model exhibits competitive results in classifying complex histopathology images of breast cancer, with a recall value of 97.73% for the cancer class in both the full training and fine-tuning approaches. This research highlights the effectiveness of ensemble learning in medical image classification, paving the way for more accurate and reliable tools in the diagnosis of breast cancer.
Emami, Z., & Shamsi, M. (2017). A review of breast cancer tumor detection methods using machine learning techniques. 2018 World Congress on Intelligent Technologies, Tehran, Conference Secretariat.
Tavakoli, B., & Nourafza, N. (2016). A review of several breast cancer prediction methods. International Conference on Engineering and Computer Science, Najafabad, Islamic Azad University, Najafabad Branch.
Lahoura, V., Singh, H., Aggarwal, A., Sharma, B., Mohammed, M. A., Damaševičius, R., ... & Cengiz, K. (2021). Cloud computing-based framework for breast cancer diagnosis using extreme learning machine. Diagnostics, 11(2), 241. https://doi.org/10.3390/diagnostics11020241
Lei, Y., Chen, X., Min, M., & Xie, Y. (2020). A semi-supervised Laplacian extreme learning machine and feature fusion with CNN for industrial superheat identification. Neurocomputing, 381, 186-195. https://doi.org/10.1016/j.neucom.2019.11.012
Li, Y., Wu, J., & Wu, Q. (2019). Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access, 7, 21400-21408. https://doi.org/10.1109/ACCESS.2019.2898044
Rozehbahani, M., Ghasemzadeh, M.R., & Maghouli, K. (2015). Prediction of breast cancer types using data mining techniques and their comparison. National Conference on Technology, Energy, and Data with an Electrical and Computer Engineering Approach, Kermanshah, Iranian Association of Electrical and Electronics Engineers - Western Branch.
Shamrat, F. J. M., Raihan, M. A., Rahman, A. S., Mahmud, I., & Akter, R. (2020). An analysis on breast disease prediction using machine learning approaches. International Journal of Scientific & Technology Research, 9(02), 2450-2455.
Gayathri, B. M., & Sumathi, C. P. (2016, December). Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer. In 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCIC.2016.7919576
Yue, W., Wang, Z., Chen, H., Payne, A., & Liu, X. (2018). Machine learning with applications in breast cancer diagnosis and prognosis. Designs, 2(2), 13. https://doi.org/10.3390/designs2020013
Shukla, R., Yadav, V., Pal, P. R., & Pathak, P. (2019). Machine learning techniques for detecting and predicting breast cancer. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(7), 2658-2662.
Lou, S. J., Hou, M. F., Chang, H. T., Chiu, C. C., Lee, H. H., Yeh, S. C. J., & Shi, H. Y. (2020). Machine learning algorithms to predict recurrence within 10 years after breast cancer surgery: A prospective cohort study. Cancers, 12(12), 3817. https://doi.org/10.3390/cancers12123817
Kumar, M., Khatri, S. K., & Mohammadian, M. (2020). Breast cancer identification and prognosis with machine learning techniques-An elucidative review. Journal of Interdisciplinary Mathematics, 23(2), 503-521. https://doi.org/10.1080/09720502.2020.1731963
Sarhadi, R., Akbari, R., Tahmasebi, S., & Zangouri, V. (2018). Breast cancer detection using SVM. Third International Conference on Electrical Engineering, Computer Science, and Information Technology, Hamedan, Permanent Secretariat of the Conference.
Morshedi, F., Ali Esfahani, S., & Ghasemi, L. (2015). Prediction and recurrence of breast cancer after treatment using data mining. Second National Conference on Computer Engineering and Information Technology Management, Tehran, Bouali Research Group.
Mahtabi, A., & Parvin Nia, E. (2018). Breast cancer prediction using a hybrid algorithm of imperialist competitive algorithm and random forest. Fourth National Conference on Soft Computing in Electrical and Computer Engineering, Ardabil, Islamic Azad University, Ardabil Branch.
Antropova, N., Huynh, B. Q., & Giger, M. L. (2017). A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Medical physics, 44(10), 5162-5171. https://doi.org/10.1002/mp.12453
Ghosh, P., Azam, S., Hasib, K. M., Karim, A., Jonkman, M., & Anwar, A. (2021, July). A performance-based study on deep learning algorithms in the effective prediction of breast cancer. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534293
Mahvash, P., & Ghaemi, R. (2016). A review of breast cancer detection methods. First National Conference on Computer Engineering, Computer Science, and Information Technology, Qom, University of Applied Science and Technology, Qom Governorate.
Zarrabi Baboldasht, Sh., & Behzadfar, N. (2021). Breast cancer detection from mammographic microcalcifications using fuzzy clustering and neural networks. Advanced Signal Processing Semiannual Journal, Vol. 5, No. 1.
Ranjouri, R., Haji Alilou, H., & Ranjouri, Y. (2022). Automatic image classification for breast cancer detection in women using artificial neural networks. Second International Conference on Mechanics, Electrical Engineering, Aerospace Engineering, and Engineering Sciences.
Khorang, H. (2022). Breast cancer detection using a fuzzy neural system optimized with the ICA algorithm. Fourteenth National Conference on Electrical, Computer, and Mechanical Engineering, Shirvan.
Nasiri, H., Gharaeian, H., & Gharaeian, K. (2021). A novel approach based on deep learning and the XGBoost algorithm for benign and malignant breast cancer detection. Eighth National Congress on New Advances in Electrical and Computer Engineering of Iran, Tehran.
Agarwal, R., & Sharma, H. (2021). A New Enhanced Recurrent Extreme Learning Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection. In Advances in Computer, Communication and Computational Sciences (pp. 461-471). Springer, Singapore. https://doi.org/10.1007/978-981-15-4409-5_42
Mahmood, T., Arsalan, M., Owais, M., Lee, M. B., & Park, K. R. (2020). Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs. Journal of clinical medicine, 9(3), 749. https://doi.org/10.3390/jcm9030749
Seryasat, O. R., & Haddadnia, J. (2018). Evaluation of a new ensemble learning framework for mass classification in mammograms. Clinical breast cancer, 18(3), e407-e420. https://doi.org/10.1016/j.clbc.2017.05.009
Zadeh, H. G., Haddadnia, J., Seryasat, O. R., & Isfahani, S. M. M. (2016). Segmenting breast cancerous regions in thermal images using fuzzy active contours. EXCLI journal, 15, 532.
Rahmani-Seryasat, O., Haddadnia, J., & Ghayoumi-Zadeh, H. (2015). A new method to classify breast cancer tumors and their fractionation. Ciência e Natura, 37(4), 51-57. https://doi.org/10.5902/2179460X19428
Touahri, R., AzizI, N., Hammami, N. E., Aldwairi, M., & Benaida, F. (2019, April). Automated breast tumor diagnosis using local binary patterns (LBP) based on deep learning classification. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCISci.2019.8716428
Wang, P., Song, Q., Li, Y., Lv, S., Wang, J., Li, L., & Zhang, H. (2020). Cross-task extreme learning machine for breast cancer image classification with deep convolutional features. Biomedical Signal Processing and Control, 57, 101789. https://doi.org/10.1016/j.bspc.2019.101789
Tumuluru, P., Lakshmi, C. P., Sahaja, T., & Prazna, R. (2019, December). A review of Machine Learning techniques for breast cancer diagnosis in medical applications. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 618-623). IEEE. https://doi.org/10.1109/I-SMAC47947.2019.9032427
Nallamala, S. H., Mishra, P., & Koneru, S. V. (2019). Breast cancer detection using machine learning way. Int J Recent Technol Eng, 8(2-3), 1402-1405. https://doi.org/10.35940/ijrte.B1260.0782S319
Shirin Manesh,M. and Razmavar,V. (2024). Breast Cancer Histopathology Image Classification Using a Set of Deep Learning Models and VGG16 and VGG19 Architectures. Transactions on Machine Intelligence, 7(4), 246-256. doi: 10.47176/TMI.2024.246
MLA
Shirin Manesh,M. , and Razmavar,V. . "Breast Cancer Histopathology Image Classification Using a Set of Deep Learning Models and VGG16 and VGG19 Architectures", Transactions on Machine Intelligence, 7, 4, 2024, 246-256. doi: 10.47176/TMI.2024.246
HARVARD
Shirin Manesh M., Razmavar V. (2024). 'Breast Cancer Histopathology Image Classification Using a Set of Deep Learning Models and VGG16 and VGG19 Architectures', Transactions on Machine Intelligence, 7(4), pp. 246-256. doi: 10.47176/TMI.2024.246
CHICAGO
M. Shirin Manesh and V. Razmavar, "Breast Cancer Histopathology Image Classification Using a Set of Deep Learning Models and VGG16 and VGG19 Architectures," Transactions on Machine Intelligence, 7 4 (2024): 246-256, doi: 10.47176/TMI.2024.246
VANCOUVER
Shirin Manesh M., Razmavar V. Breast Cancer Histopathology Image Classification Using a Set of Deep Learning Models and VGG16 and VGG19 Architectures. Trans. Mach. Intell., 2024; 7(4): 246-256. doi: 10.47176/TMI.2024.246
