Treffer: A Dirichlet Distribution-Based Complex Ensemble Approach for Breast Cancer Classification from Ultrasound Images with Transfer Learning and Multiphase Spaced Repetition Method.
Title:
A Dirichlet Distribution-Based Complex Ensemble Approach for Breast Cancer Classification from Ultrasound Images with Transfer Learning and Multiphase Spaced Repetition Method.
Authors:
Güler O; Department of Computer Engineering, Çankırı Karatekin University, Çankırı, Turkey. osmanguler@karatekin.edu.tr.
Source:
Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 Feb; Vol. 39 (1), pp. 202-228. Date of Electronic Publication: 2025 Apr 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Cham, Switzerland] : Springer Nature, [2024]-
MeSH Terms:
References:
World Health Organization. (2020). WHO report on cancer: Setting priorities, investing wisely and providing care for all. (accessed October 8, 2022).
Cancer Today. (n.d.). https://gco.iarc.fr/today/home (accessed October 8, 2022).
Zhu, Y. C., AlZoubi, A., Jassim, S., & Jiang, Q., et al. (2021). A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics, 110, 106300. (PMID: 33232887)
Balaha, H. M., Saif, M., Tamer, A., & Abdelhay, E. H. (2022). Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer. Neural Computing and Applications, 34(11), 8671–8695.
Singh, B. K., Verma, K., & Thoke, A. S. (2016). Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Systems with Applications, 66, 114–123.
Misra, S., Jeon, S., Managuli, R., & Lee, S., et al. (2021). Bi-modal transfer learning for classifying breast cancers via combined B-mode and ultrasound strain imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(1), 222–232. (PMID: 34633928)
Dabass, M., Vashisth, S., & Vig, R. (2022). A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Computers in Biology and Medicine, 147, 105680. (PMID: 35671654)
Gupta, K., & Chawla, N. (2020). Analysis of histopathological images for prediction of breast cancer using traditional classifiers with pre-trained CNN. Procedia Computer Science, 167, 878–889.
Calp, M. H. (2021). Use of deep learning approaches in cancer diagnosis. In U. Kose & J. Alzubi (Eds.), Deep Learning for Cancer Diagnosis (pp. 249–267). Springer, Singapore.
Savaş, S. (2022). Detecting the stages of Alzheimer’s disease with pre-trained deep learning architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218.
Buyrukoğlu, S. (2021). Early detection of Alzheimer’s disease using data mining: Comparison of ensemble feature selection approaches. Konya Journal of Engineering Sciences, 9(1), 50–61.
Asiri, N., Hussain, M., Al Adel, F., & Alzaidi, N. (2019). Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artificial Intelligence in Medicine, 99, 101701. (PMID: 31606116)
Buyrukoğlu, S., & Akbaş, A. (2022). Machine learning-based early prediction of type 2 diabetes: A new hybrid feature selection approach using correlation matrix with heatmap and SFS. Balkan Journal of Electrical and Computer Engineering, 10(2), 110–117. https://doi.org/10.17694/bajece.973129.
Alafif, T., Tehame, A. M., Bajaba, S., Barnawi, A., & Zia, S. (2021). Machine and deep learning towards COVID-19 diagnosis and treatment: Survey, challenges, and future directions. International Journal of Environmental Research and Public Health, 18(3), 1117. (PMID: 335139847908539)
Bütüner, R., & Calp, M. H. (2022). Diagnosis and detection of COVID-19 from lung tomography images using deep learning and machine learning methods.International Journal of Intelligent Systems and Applications in Engineering, 10(2), 190–200. https://ijisae.org/index.php/IJISAE/article/view/1843.
Savaş, S., Topaloğlu, N., Kazcı, O., & Koşar, P. N. (2019). Classification of carotid artery intima media thickness ultrasound images with deep learning. Journal of Medical Systems, 43(8), 1–12.
Al-Saedi, D. K. A., & Savaş, S. (2022). Classification of skin cancer with deep transfer learning method. In Computer Science, IDAP-2022: International Artificial Intelligence and Data Processing Symposium, 202–210.
Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. C. (2019). A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1–6.
Lu, J., Behbood, V., Hao, P., Zuo, H., et al. (2015). Transfer learning using computational intelligence: A survey. Knowledge-Based Systems, 80, 14–23.
Salehi, A. W., Khan, S., Gupta, G., & Alabduallah, B. I., et al. (2023). A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability, 15(7), 5930.
Wu, W. J., Lin, S. W., & Moon, W. K. (2012). Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Computerized Medical Imaging and Graphics, 36(8), 627–633. (PMID: 22939834)
Boldbaatar, E. A., Lin, L. Y., & Lin, C. M. (2019). Breast tumor classification using fast convergence recurrent wavelet Elman neural networks. Neural Processing Letters, 50(3), 2037–2052.
Acharya, U. R., Meiburger, K. M., Koh, J. E. W., Ciaccio, E. J., et al. (2019). A novel algorithm for breast lesion detection using textons and local configuration pattern features with ultrasound imagery. IEEE Access, 7, 22829–22842.
Pavithra, S., Vanithamani, R., & Justin, J. (2020). Computer aided breast cancer detection using ultrasound images. Materials Today: Proceedings, 33, 4802–4807.
Moon, W. K., Lee, Y. W., Ke, H. H., Lee, S. H., et al. (2020). Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190, 105361. (PMID: 32007839)
Zeimarani, B., Costa, M. G. F., Nurani, N. Z., & Bianco, S. R., et al. (2020). Breast lesion classification in ultrasound images using deep convolutional neural network. IEEE Access, 8, 133349–133359.
Yang, Z., Gong, X., Guo, Y., & Liu, W. (2020). A temporal sequence dual-branch network for classifying hybrid ultrasound data of breast cancer. IEEE Access, 8, 82688–82699.
Masud, M., Eldin Rashed, A. E., & Hossain, M. S. (2020). Convolutional neural network-based models for diagnosis of breast cancer. Neural Computing and Applications, 34, 11383–11394. (PMID: 330521727545025)
Zhuang, Z., Kang, Y., Joseph Raj, A. N., Yuan, Y., et al. (2020). Breast ultrasound lesion classification based on image decomposition and transfer learning. Medical Physics, 47(12), 6257–6269. (PMID: 33012047)
Zhuang, Z., Yang, Z., Raj, A. N. J., Wei, C., et al. (2021). Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Computer Methods and Programs in Biomedicine, 208, 106221. (PMID: 34144251)
Eroğlu, Y., Yildirim, M., & Çınar, A. (2021). Convolutional neural networks-based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Computers in Biology and Medicine, 133, 104407. (PMID: 33901712)
Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2021). Breast ultrasound tumour classification: A machine learning—Radiomics-based approach. Expert Systems, 38(5), e12701.
Joshi, R. C., Singh, D., Tiwari, V., & Dutta, M. K. (2022). An efficient deep neural network based abnormality detection and multi-class breast tumor classification. Multimedia Tools and Applications, 81(10), 13691-13711.
Karthik, R., Menaka, R., Kathiresan, G. S., Anirudh, M., & Nagharjun, M. (2022). Gaussian Dropout Based Stacked Ensemble CNN for Classification of Breast Tumor in Ultrasound Images. IRBM, 43(6), 715-733.
Lu, S. Y., Wang, S. H., & Zhang, Y. D. (2022). SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection. Computers in Biology and Medicine, 148, 105812. (PMID: 35834967)
Qu, X., Lu, H., Tang, W., Wang, S., & et al. (2022). A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images. Medical Physics, 49(9), 5787-5798.
Podda, A. S., Balia, R., Barra, S., Carta, S., & et al. (2022). Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images. Journal of Computational Science, 63, 101816.
Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2022). Achieving highly efficient breast ultrasound tumor classification with deep convolutional neural networks. International Journal of Information Technology, 14, 3311-3320.
Sahu, A., Das, P. K., & Meher, S. (2023). High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomedical Signal Processing and Control, 80, 104292.
Gupta, S., Agrawal, S., Singh, S. K., & Kumar, S. (2023). A novel transfer learning-based model for ultrasound breast cancer image classification. In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022 (pp. 511–523). Singapore: Springer Nature Singapore.
Özcan, H. (2023). BUS-CAD: A computer-aided diagnosis system for breast tumor classification in ultrasound images using grid-search-optimized machine learning algorithms with extended and Boruta-selected features. International Journal of Imaging Systems and Technology, 33(5), 1480-1493.
Sirjani, N., Oghli, M. G., Tarzamni, M. K., Gity, M., Shabanzadeh, A., Ghaderi, P., & Taghipour, M. (2023). A novel deep learning model for breast lesion classification using ultrasound images: A multicenter data evaluation. Physica Medica, 107, 102560. (PMID: 36878133)
Dar, M. F., & Ganivada, A. (2023). Efficientu-net: A novel deep learning method for breast tumor segmentation and classification in ultrasound images. Neural Processing Letters, 55(8), 10439-10462.
Mo, Y., Han, C., Liu, Y., Liu, M., Shi, Z., Lin, J., & Liang, C. (2023). Hover-trans: Anatomy-aware hover-transformer for roi-free breast cancer diagnosis in ultrasound images. IEEE Transactions on Medical Imaging, 42(6), 1696-1706. (PMID: 37018705)
Afrifa, S., Varadarajan, V., Appiahene, P., & Zhang, T. (2023). A novel artificial intelligence techniques for women breast cancer classification using ultrasound images. Clinical and Experimental Obstetrics and Gynecology, 50(12), 271.
Cruz-Ramos, C., García-Ávila, O., Almaraz-Damián, J. A., Ponomaryov, V., Reyes-Reyes, R., & Sadovnychiy, S. (2023). Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features. Entropy, 25(7), 991. (PMID: 3750993810378567)
Islam, M. R., Rahman, M. M., Ali, M. S., Nafi, A. A. N., Alam, M. S., Godder, T. K., & Islam, M. K. (2024). Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images. Machine Learning with Applications, 16, 100555.
Işık, G., & Pacal, İ. (2024). Few-shot classification of ultrasound breast cancer images using meta-learning algorithms. Neural Computing and Applications, 1–13.
Dar, M. F., & Ganivada, A. (2024). Deep learning and genetic algorithm-based ensemble model for feature selection and classification of breast ultrasound images. Image and Vision Computing, 146, 105018.
Sahu, A., Das, P. K., & Meher, S. (2024). An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images. Biomedical Signal Processing and Control, 87, 105377.
Meng, X., Ma, J., Liu, F., Chen, Z., & Zhang, T. (2024). An interpretable breast ultrasound image classification algorithm based on convolutional neural network and transformer. Mathematics, 12(15), 2354.
AlZoubi, A., Lu, F., Zhu, Y., Ying, T., Ahmed, M., & Du, H. (2024). Classification of breast lesions in ultrasound images using deep convolutional neural networks: Transfer learning versus automatic architecture design. Medical and Biological Engineering and Computing, 62(1), 135-149. (PMID: 37735296)
Jiménez-Gaona, Y., Álvarez, M. J. R., Castillo-Malla, D., García-Jaén, S., Carrión-Figueroa, D., Corral-Domínguez, P., & Lakshminarayanan, V. (2024). BraNet: A mobile application for breast image classification based on deep learning algorithms. Medical and Biological Engineering and Computing, 1–20.
Atrey, K., Singh, B. K., & Bodhey, N. K. (2024). Multimodal classification of breast cancer using feature level fusion of mammogram and ultrasound images in machine learning paradigm. Multimedia Tools and Applications, 83(7), 21347-21368.
Lanjewar, M. G., Panchbhai, K. G., & Patle, L. B. (2024). Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images. Computers in Biology and Medicine, 169, 107914. (PMID: 38190766)
Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020). Dataset of breast ultrasound images. Data in Brief, 28, 104863. (PMID: 31867417)
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., et al. (2015). Going deeper with convolutions.In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826).
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556 .
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition.In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
Madsen, R. E., Kauchak, D., & Elkan, C. (2005). Modeling word burstiness using the Dirichlet distribution. In Proceedings of the 22nd international conference on Machine learning (pp. 545–552).
Cancer Today. (n.d.). https://gco.iarc.fr/today/home (accessed October 8, 2022).
Zhu, Y. C., AlZoubi, A., Jassim, S., & Jiang, Q., et al. (2021). A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics, 110, 106300. (PMID: 33232887)
Balaha, H. M., Saif, M., Tamer, A., & Abdelhay, E. H. (2022). Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer. Neural Computing and Applications, 34(11), 8671–8695.
Singh, B. K., Verma, K., & Thoke, A. S. (2016). Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Systems with Applications, 66, 114–123.
Misra, S., Jeon, S., Managuli, R., & Lee, S., et al. (2021). Bi-modal transfer learning for classifying breast cancers via combined B-mode and ultrasound strain imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(1), 222–232. (PMID: 34633928)
Dabass, M., Vashisth, S., & Vig, R. (2022). A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Computers in Biology and Medicine, 147, 105680. (PMID: 35671654)
Gupta, K., & Chawla, N. (2020). Analysis of histopathological images for prediction of breast cancer using traditional classifiers with pre-trained CNN. Procedia Computer Science, 167, 878–889.
Calp, M. H. (2021). Use of deep learning approaches in cancer diagnosis. In U. Kose & J. Alzubi (Eds.), Deep Learning for Cancer Diagnosis (pp. 249–267). Springer, Singapore.
Savaş, S. (2022). Detecting the stages of Alzheimer’s disease with pre-trained deep learning architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218.
Buyrukoğlu, S. (2021). Early detection of Alzheimer’s disease using data mining: Comparison of ensemble feature selection approaches. Konya Journal of Engineering Sciences, 9(1), 50–61.
Asiri, N., Hussain, M., Al Adel, F., & Alzaidi, N. (2019). Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artificial Intelligence in Medicine, 99, 101701. (PMID: 31606116)
Buyrukoğlu, S., & Akbaş, A. (2022). Machine learning-based early prediction of type 2 diabetes: A new hybrid feature selection approach using correlation matrix with heatmap and SFS. Balkan Journal of Electrical and Computer Engineering, 10(2), 110–117. https://doi.org/10.17694/bajece.973129.
Alafif, T., Tehame, A. M., Bajaba, S., Barnawi, A., & Zia, S. (2021). Machine and deep learning towards COVID-19 diagnosis and treatment: Survey, challenges, and future directions. International Journal of Environmental Research and Public Health, 18(3), 1117. (PMID: 335139847908539)
Bütüner, R., & Calp, M. H. (2022). Diagnosis and detection of COVID-19 from lung tomography images using deep learning and machine learning methods.International Journal of Intelligent Systems and Applications in Engineering, 10(2), 190–200. https://ijisae.org/index.php/IJISAE/article/view/1843.
Savaş, S., Topaloğlu, N., Kazcı, O., & Koşar, P. N. (2019). Classification of carotid artery intima media thickness ultrasound images with deep learning. Journal of Medical Systems, 43(8), 1–12.
Al-Saedi, D. K. A., & Savaş, S. (2022). Classification of skin cancer with deep transfer learning method. In Computer Science, IDAP-2022: International Artificial Intelligence and Data Processing Symposium, 202–210.
Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. C. (2019). A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1–6.
Lu, J., Behbood, V., Hao, P., Zuo, H., et al. (2015). Transfer learning using computational intelligence: A survey. Knowledge-Based Systems, 80, 14–23.
Salehi, A. W., Khan, S., Gupta, G., & Alabduallah, B. I., et al. (2023). A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability, 15(7), 5930.
Wu, W. J., Lin, S. W., & Moon, W. K. (2012). Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Computerized Medical Imaging and Graphics, 36(8), 627–633. (PMID: 22939834)
Boldbaatar, E. A., Lin, L. Y., & Lin, C. M. (2019). Breast tumor classification using fast convergence recurrent wavelet Elman neural networks. Neural Processing Letters, 50(3), 2037–2052.
Acharya, U. R., Meiburger, K. M., Koh, J. E. W., Ciaccio, E. J., et al. (2019). A novel algorithm for breast lesion detection using textons and local configuration pattern features with ultrasound imagery. IEEE Access, 7, 22829–22842.
Pavithra, S., Vanithamani, R., & Justin, J. (2020). Computer aided breast cancer detection using ultrasound images. Materials Today: Proceedings, 33, 4802–4807.
Moon, W. K., Lee, Y. W., Ke, H. H., Lee, S. H., et al. (2020). Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190, 105361. (PMID: 32007839)
Zeimarani, B., Costa, M. G. F., Nurani, N. Z., & Bianco, S. R., et al. (2020). Breast lesion classification in ultrasound images using deep convolutional neural network. IEEE Access, 8, 133349–133359.
Yang, Z., Gong, X., Guo, Y., & Liu, W. (2020). A temporal sequence dual-branch network for classifying hybrid ultrasound data of breast cancer. IEEE Access, 8, 82688–82699.
Masud, M., Eldin Rashed, A. E., & Hossain, M. S. (2020). Convolutional neural network-based models for diagnosis of breast cancer. Neural Computing and Applications, 34, 11383–11394. (PMID: 330521727545025)
Zhuang, Z., Kang, Y., Joseph Raj, A. N., Yuan, Y., et al. (2020). Breast ultrasound lesion classification based on image decomposition and transfer learning. Medical Physics, 47(12), 6257–6269. (PMID: 33012047)
Zhuang, Z., Yang, Z., Raj, A. N. J., Wei, C., et al. (2021). Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Computer Methods and Programs in Biomedicine, 208, 106221. (PMID: 34144251)
Eroğlu, Y., Yildirim, M., & Çınar, A. (2021). Convolutional neural networks-based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Computers in Biology and Medicine, 133, 104407. (PMID: 33901712)
Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2021). Breast ultrasound tumour classification: A machine learning—Radiomics-based approach. Expert Systems, 38(5), e12701.
Joshi, R. C., Singh, D., Tiwari, V., & Dutta, M. K. (2022). An efficient deep neural network based abnormality detection and multi-class breast tumor classification. Multimedia Tools and Applications, 81(10), 13691-13711.
Karthik, R., Menaka, R., Kathiresan, G. S., Anirudh, M., & Nagharjun, M. (2022). Gaussian Dropout Based Stacked Ensemble CNN for Classification of Breast Tumor in Ultrasound Images. IRBM, 43(6), 715-733.
Lu, S. Y., Wang, S. H., & Zhang, Y. D. (2022). SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection. Computers in Biology and Medicine, 148, 105812. (PMID: 35834967)
Qu, X., Lu, H., Tang, W., Wang, S., & et al. (2022). A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images. Medical Physics, 49(9), 5787-5798.
Podda, A. S., Balia, R., Barra, S., Carta, S., & et al. (2022). Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images. Journal of Computational Science, 63, 101816.
Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2022). Achieving highly efficient breast ultrasound tumor classification with deep convolutional neural networks. International Journal of Information Technology, 14, 3311-3320.
Sahu, A., Das, P. K., & Meher, S. (2023). High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomedical Signal Processing and Control, 80, 104292.
Gupta, S., Agrawal, S., Singh, S. K., & Kumar, S. (2023). A novel transfer learning-based model for ultrasound breast cancer image classification. In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022 (pp. 511–523). Singapore: Springer Nature Singapore.
Özcan, H. (2023). BUS-CAD: A computer-aided diagnosis system for breast tumor classification in ultrasound images using grid-search-optimized machine learning algorithms with extended and Boruta-selected features. International Journal of Imaging Systems and Technology, 33(5), 1480-1493.
Sirjani, N., Oghli, M. G., Tarzamni, M. K., Gity, M., Shabanzadeh, A., Ghaderi, P., & Taghipour, M. (2023). A novel deep learning model for breast lesion classification using ultrasound images: A multicenter data evaluation. Physica Medica, 107, 102560. (PMID: 36878133)
Dar, M. F., & Ganivada, A. (2023). Efficientu-net: A novel deep learning method for breast tumor segmentation and classification in ultrasound images. Neural Processing Letters, 55(8), 10439-10462.
Mo, Y., Han, C., Liu, Y., Liu, M., Shi, Z., Lin, J., & Liang, C. (2023). Hover-trans: Anatomy-aware hover-transformer for roi-free breast cancer diagnosis in ultrasound images. IEEE Transactions on Medical Imaging, 42(6), 1696-1706. (PMID: 37018705)
Afrifa, S., Varadarajan, V., Appiahene, P., & Zhang, T. (2023). A novel artificial intelligence techniques for women breast cancer classification using ultrasound images. Clinical and Experimental Obstetrics and Gynecology, 50(12), 271.
Cruz-Ramos, C., García-Ávila, O., Almaraz-Damián, J. A., Ponomaryov, V., Reyes-Reyes, R., & Sadovnychiy, S. (2023). Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features. Entropy, 25(7), 991. (PMID: 3750993810378567)
Islam, M. R., Rahman, M. M., Ali, M. S., Nafi, A. A. N., Alam, M. S., Godder, T. K., & Islam, M. K. (2024). Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images. Machine Learning with Applications, 16, 100555.
Işık, G., & Pacal, İ. (2024). Few-shot classification of ultrasound breast cancer images using meta-learning algorithms. Neural Computing and Applications, 1–13.
Dar, M. F., & Ganivada, A. (2024). Deep learning and genetic algorithm-based ensemble model for feature selection and classification of breast ultrasound images. Image and Vision Computing, 146, 105018.
Sahu, A., Das, P. K., & Meher, S. (2024). An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images. Biomedical Signal Processing and Control, 87, 105377.
Meng, X., Ma, J., Liu, F., Chen, Z., & Zhang, T. (2024). An interpretable breast ultrasound image classification algorithm based on convolutional neural network and transformer. Mathematics, 12(15), 2354.
AlZoubi, A., Lu, F., Zhu, Y., Ying, T., Ahmed, M., & Du, H. (2024). Classification of breast lesions in ultrasound images using deep convolutional neural networks: Transfer learning versus automatic architecture design. Medical and Biological Engineering and Computing, 62(1), 135-149. (PMID: 37735296)
Jiménez-Gaona, Y., Álvarez, M. J. R., Castillo-Malla, D., García-Jaén, S., Carrión-Figueroa, D., Corral-Domínguez, P., & Lakshminarayanan, V. (2024). BraNet: A mobile application for breast image classification based on deep learning algorithms. Medical and Biological Engineering and Computing, 1–20.
Atrey, K., Singh, B. K., & Bodhey, N. K. (2024). Multimodal classification of breast cancer using feature level fusion of mammogram and ultrasound images in machine learning paradigm. Multimedia Tools and Applications, 83(7), 21347-21368.
Lanjewar, M. G., Panchbhai, K. G., & Patle, L. B. (2024). Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images. Computers in Biology and Medicine, 169, 107914. (PMID: 38190766)
Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020). Dataset of breast ultrasound images. Data in Brief, 28, 104863. (PMID: 31867417)
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., et al. (2015). Going deeper with convolutions.In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826).
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556 .
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition.In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
Madsen, R. E., Kauchak, D., & Elkan, C. (2005). Modeling word burstiness using the Dirichlet distribution. In Proceedings of the 22nd international conference on Machine learning (pp. 545–552).
Contributed Indexing:
Keywords: Breast cancer; Breast ultrasound images; Deep learning; Ensemble learning; Transfer learning
Entry Date(s):
Date Created: 20250429 Date Completed: 20260219 Latest Revision: 20260222
Update Code:
20260222
PubMed Central ID:
PMC12920884
DOI:
10.1007/s10278-025-01515-5
PMID:
40301291
Database:
MEDLINE
Weitere Informationen
Breast ultrasound is a useful and rapid diagnostic tool for the early detection of breast cancer. Artificial intelligence-supported computer-aided decision systems, which assist expert radiologists and clinicians, provide reliable and rapid results. Deep learning methods and techniques are widely used in the field of health for early diagnosis, abnormality detection, and disease diagnosis. Therefore, in this study, a deep ensemble learning model based on Dirichlet distribution using pre-trained transfer learning models for breast cancer classification from ultrasound images is proposed. In the study, experiments were conducted using the Breast Ultrasound Images Dataset (BUSI). The dataset, which had an imbalanced class structure, was balanced using data augmentation techniques. DenseNet201, InceptionV3, VGG16, and ResNet152 models were used for transfer learning with fivefold cross-validation. Statistical analyses, including the ANOVA test and Tukey HSD test, were applied to evaluate the model's performance and ensure the reliability of the results. Additionally, Grad-CAM (Gradient-weighted Class Activation Mapping) was used for explainable AI (XAI), providing visual explanations of the deep learning model's decision-making process. The spaced repetition method, commonly used to improve the success of learners in educational sciences, was adapted to artificial intelligence in this study. The results of training with transfer learning models were used as input for further training, and spaced repetition was applied using previously learned information. The use of the spaced repetition method led to increased model success and reduced learning times. The weights obtained from the trained models were input into an ensemble learning system based on Dirichlet distribution with different variations. The proposed model achieved 99.60% validation accuracy on the dataset, demonstrating its effectiveness in breast cancer classification.
(© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
Declarations. Ethics Approval: Not applicable, as this study did not involve human participants or animals. Consent to Participate: Not applicable. Consent for Publication: Not applicable. Competing interests: The author declares no competing interests.