Breast Cancer Prediction Using Score-Level Fusion of Machine Learning and Deep Learning Models
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32804
Breast Cancer Prediction Using Score-Level Fusion of Machine Learning and Deep Learning Models

Authors: [email protected]

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data need a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM), ensemble learning with hyper parameters optimization, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: Machine learning, Deep learning, cancer prediction, breast cancer, LSTM, Score-Level Fusion.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 299

References:


[1] G. Chugh, S. Kumar and N. Singh, "Survey on machine learning and deep learning applications in breast cancer diagnosis," Cognitive Computation, vol. 13, no. 6, pp. 1451-1470, 2021.
[2] H. Aljuaid, N. Alturki, N. Alsubaie, L. Cavallaro and A. Liotta, "Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning," Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning, vol. 223, p. 106951, 2022
[3] World Health Organization, WHO position paper on mammography screening, Geneva, Switzerland: WHO Library Cataloguing-in-Publication Data, 2014.
[4] world bladder cancer, "GLOBOCAN 2020: Bladder cancer 10th most commonly diagnosed worldwide," World Bladder Cancer, Lyon, France, 2020.
[5] H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. J. DMV and F. Bray, "Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries," CA: a cancer journal for clinicians, vol. 71, no. 3, pp. 209-249, 2020.
[6] S. Sarumathi, M. Vaishnavi, S. Geetha, P. Ranjetha, " Comparative Analysis of Machine Learning Tools: A Review", International Journal of Computer and Information Engineering, Vol. 15, No. 6, 2021.
[7] D. Ravi, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo and G.-Z. Yang, "Deep Learning for Health Informatics," IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4-21, 2017.
[8] P. Ferroni, F. Zanzotto, S. Riondino, N. Scarpato, F. Guadagni, & M, Roselli, "Breast cancer prognosis using a machine learning approach", Cancers, Vol. 11, no. 3, 2018.
[9] A. Ahmad and A. M. Mayya, "A new tool to predict lung cancer based on risk factors," Heliyon , vol. 6, no. 2, p. e03402, 2020.
[10] S. Khozama, A. Mayya, "Study the Effect of the Risk Factors in the Estimation of the Breast Cancer Risk Score Using Machine Learning", Asian Pacific Journal of Cancer Prevention, Vol. 22, no.11, pp.3543-3551, 2021.
[11] C. Dalmiglio, L. Brilli, M. Campanile, C. Ciuoli, A. Cartocci and M. G. Castagna, "CONUT score: a new tool for predicting prognosis in patients with advanced thyroid cancer treated with TKI," Cancers, vol. 14, no. 3, 2022.
[12] C. Huang, Q. Su, Z. Ding, W. Zeng and Z. Zhou, "A novel clinical tool to predict cancer‐specific survival in patients with primary pelvic sarcomas: A large population‐based retrospective cohort study," Cancer Medicine, 2022.
[13] S. Nagalpara and B. M. Patel, "A Deep Learning Strategy for Predicting Liver Cancer Using Convolutional Neural Network Algorithm," Indian Journal of Computer Science, vol. 7, no. 3, 2022.
[14] S. Khozama, A. Mayya, "A new range-based breast cancer prediction model using the Bayes' theorem and Ensemble learning", Information Technology and Control Journal, 2022, to be published.
[15] P. Gupta, and S. Garg, "Breast cancer prediction using varying parameters of machine learning models", Procedia Computer Science, vol. 171, pp. 593-601, 2020.
[16] M. Saii and A. Mayya, "Lung Detection and Segmentation Using Marker Watershed and Laplacian Filtering", Journal of Biomedical Engineering and Clinical Science, vol. 1, no.2, pp. 29-42, 2015.
[17] Y. Benhammou, B. Achchab, F. Herrera and S. Tabik, "BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights.," Neurocomputing, vol. 375, no. 2020, pp. 9-24, 2020.
[18] Y. A. Haşim, E. H, İ. T and K. S., "Detection of breast cancer via deep convolution neural networks using MRI images," Multimed Tools Appl., vol. 79, no. 21, pp. 15555-15573, 2019.
[19] C. CAO, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo and Z. Xie, "Deep learning and its applications in biomedicine," Genomics, proteomics & bioinformatics, vol. 16, no. 1, pp. 17-32, 2018.
[20] D. Selvathi and A. Aarthy Poornila, "Deep learning techniques for breast cancer detection using medical image analysis," in Biologically rationalized computing techniques for image processing applications., Cham, 2018.
[21] S. KHAN, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo and Z. Xie, "A novel deep learning based framework for the detection and classification of breast cancer using transfer learning," Pattern Recognition Letters, vol. 125, pp. 1-6, 2019.
[22] P. Ferroni, F.M. Zanzotto., S. Riondino, N. Scarpato, F. Guadagni, and M. Roselli, "Breast cancer prognosis using a machine learning approach", Cancers, vol. 11, no.3, pp.328, 2019.
[23] D. M. Lang, J. C. Peeken, S. E. Combs, J. J. Wilkens and S. Bartzsch, "Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients," Cancers, vol. 13, no. 786, pp. 1-11, 2021.
[24] N. Ashokkumar, S. Meera, P. Anandan, M. Yaswanth, B. Murthy, T. A. Alahmadi, S. A. Alharbi, S. S. Raghavan and A. Jayadhas, "Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer," BioMed Research International, vol. 2022, pp. 1-14, 2022.
[25] H. Saleh, H. Alyami, and W. Alosaimi, "Predicting Breast Cancer Based on Optimized Deep Learning Approach", Computational Intelligence and Neuroscience, special issue, 2022.
[26] K. Greff, R. Srivastava K., J. Koutník, B. Steunebrink and J. Schmidhuber, "LSTM: A search space odyssey", IEEE transactions on neural networks and learning systems, vol. 28, no. 10, pp.2222-2232, 2016.
[27] O. Sagi, and L. Rokach, 'Ensemble learning: A survey", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Rokach, vol. 8, no. 4, 2018.