ML-DSTnet: : A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster–Shafer Theory
Authors: 
Mohsen Eftekharian, Ali Nodehi, Rasul Enayatifar Academic Editor: Dalin ZhangAuthors Info & Claims
Mohsen Eftekharian, Ali Nodehi, Rasul Enayatifar Academic Editor: Dalin ZhangAuthors Info & ClaimsAbstract
Medical intelligence detection systems have changed with the help of artificial intelligence and have also faced challenges. Breast cancer diagnosis and classification are part of this medical intelligence system. Early detection can lead to an increase in treatment options. On the other hand, uncertainty is a case that has always been with the decision-maker. The system’s parameters cannot be accurately estimated, and the wrong decision is made. To solve this problem, we have proposed a method in this article that reduces the ignorance of the problem with the help of Dempster–Shafer theory so that we can make a better decision. This research on the MIAS dataset, based on image processing machine learning and Dempster–Shafer mathematical theory, tries to improve the diagnosis and classification of benign, malignant masses. We first determine the results of the diagnosis of mass type with MLP by using the texture feature and CNN. We combine the results of the two classifications with Dempster–Shafer theory and improve its accuracy. The obtained results show that the proposed approach has better performance than others based on evaluation criteria such as accuracy of 99.10%, sensitivity of 98.4%, and specificity of 100%.
References
[1]
C. Fitzmaurice, C. Allen, R. M. Barber, L. Barregard, Z. A. Bhutta, H. Brenner, D. J. Dicker, O. Chimed-Orchir, R. Dandona, L. Dandona, T. Fleming, M. H. Forouzanfar, J. Hancock, R. J. Hay, R. Hunter-Merrill, C. Huynh, H. D. Hosgood, C. O. Johnson, J. B. Jonas, J. Khubchandani, G. A. Kumar, M. Kutz, Q. Lan, H. J. Larson, X. Liang, S. S. Lim, A. D. Lopez, M. F. MacIntyre, L. Marczak, N. Marquez, A. H. Mokdad, C. Pinho, F. Pourmalek, J. A. Salomon, J. R. Sanabria, L. Sandar, B. Sartorius, S. M. Schwartz, K. A. Shackelford, K. Shibuya, J. Stanaway, C. Steiner, J. Sun, K. Takahashi, S. E. Vollset, T. Vos, J. A. Wagner, H. Wang, R. Westerman, H. Zeeb, L. Zoeckler, F. Abd-Allah, M. B. Ahmed, S. Alabed, N. K. Alam, S. F. Aldhahri, G. Alem, M. A. Alemayohu, R. Ali, R. Al-Raddadi, A. Amare, Y. Amoako, A. Artaman, H. Asayesh, N. Atnafu, A. Awasthi, H. B. Saleem, A. Barac, N. Bedi, I. Bensenor, A. Berhane, E. Bernabé, B. Betsu, A. Binagwaho, D. Boneya, I. Campos-Nonato, C. Castañeda-Orjuela, F. Catalá-López, P. Chiang, C. Chibueze, A. Chitheer, J. Y. Choi, B. Cowie, S. Damtew, J. das Neves, S. Dey, S. Dharmaratne, P. Dhillon, E. Ding, T. Driscoll, D. Ekwueme, A. Y. Endries, M. Farvid, F. Farzadfar, J. Fernandes, F. Fischer, T. T. G/Hiwot, A. Gebru, S. Gopalani, A. Hailu, M. Horino, N. Horita, A. Husseini, I. Huybrechts, M. Inoue, F. Islami, M. Jakovljevic, S. James, M. Javanbakht, S. H. Jee, A. Kasaeian, M. S. Kedir, Y. S. Khader, Y. H. Khang, D. Kim, J. Leigh, S. Linn, R. Lunevicius, H. M. A. El Razek, R. Malekzadeh, D. C. Malta, W. Marcenes, D. Markos, Y. A. Melaku, K. G. Meles, W. Mendoza, D. T. Mengiste, T. J. Meretoja, T. R. Miller, K. A. Mohammad, A. Mohammadi, S. Mohammed, M. Moradi-Lakeh, G. Nagel, D. Nand, Q. Le Nguyen, S. Nolte, F. A. Ogbo, K. E. Oladimeji, E. Oren, M. Pa, E. K. Park, D. M. Pereira, D. Plass, M. Qorbani, A. Radfar, A. Rafay, M. Rahman, S. M. Rana, K. Søreide, M. Satpathy, M. Sawhney, S. G. Sepanlou, M. A. Shaikh, J. She, I. Shiue, H. R. Shore, M. G. Shrime, S. So, S. Soneji, V. Stathopoulou, K. Stroumpoulis, M. B. Sufiyan, B. L. Sykes, R. Tabarés-Seisdedos, F. Tadese, B. A. Tedla, G. A. Tessema, J. S. Thakur, B. X. Tran, K. N. Ukwaja, B. S. C. Uzochukwu, V. V. Vlassov, E. Weiderpass, M. Wubshet Terefe, H. G. Yebyo, H. H. Yimam, N. Yonemoto, M. Z. Younis, C. Yu, Z. Zaidi, M. E. S. Zaki, Z. M. Zenebe, C. J. L. Murray, and M. Naghavi, “Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study,” JAMA Oncology, vol. 3, no. 4, pp. 524–548, 2017.
[2]
M. R. Nathan and P. Schmid, “The emerging world of breast cancer immunotherapy,” The Breast, vol. 37, pp. 200–206, 2018.
[3]
G. Muhammad, M. S. Hossain, and N. Kumar, “EEG-based pathology detection for home health monitoring,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, pp. 603–610, 2021.
[4]
D. R. Youlden, S. M. Cramb, N. A. Dunn, J. M. Muller, C. M. Pyke, and P. D. Baade, “The descriptive epidemiology of female breast cancer: an international comparison of screening, incidence, survival and mortality,” Cancer Epidemiology, vol. 36, no. 3, pp. 237–248, 2012.
[5]
N. Houssami and K. Hunter, “The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening,” NPJ Breast Cancer, vol. 3, no. 1, p. 12, 2017.
[6]
N. Kavya, N. Sriraam, N. Usha, B. Hiremath, A. Suresh, D. Sharath, Venkatraman B, and Menaka M, “Breast cancer lesion detection from cranial-caudal view of mammogram images using statistical and texture features extraction,” International Journal of Biomedical and Clinical Engineering, vol. 9, no. 1, pp. 16–32, 2020.
[7]
L. Barinov, A. Jairaj, M. Becker, S. Seymour, E. Lee, A. Schram, E. Lane, A. Goldszal, D. Quigley, and L. Paster, “Impact of data presentation on physician performance utilizing artificial intelligence-based computer-aided diagnosis and decision support systems,” Journal of Digital Imaging, vol. 32, no. 3, pp. 408–416, 2019.
[8]
E. Keavey, N. Phelan, and P. Fitzpatrick, “Clinical performance of digital mammography systems in a breast screening programme – an update,” Physica Medica, vol. 52, pp. 179–180, 2018.
[9]
N. Houssami, D. Bernardi, M. Pellegrini, M. Valentini, C. Fantò, L. Ostillio, P. Tuttobene, A. Luparia, and P. Macaskill, “Breast cancer detection using single-reading of breast tomosynthesis (3D-mammography) compared to double-reading of 2D-mammography: evidence from a population-based trial,” Cancer Epidemiology, vol. 47, pp. 94–99, 2017.
[10]
M. Masud, A. E. Eldin Rashed, and M. S. Hossain, “Convolutional neural network-based models for diagnosis of breast cancer,” Neural Computing & Applications, vol. 34, no. 14, pp. 11383–11394, 2020.
[11]
S. A. Alanazi, M. M. Kamruzzaman, M. Alruwaili, N. Alshammari, S. A. Alqahtani, and A. Karime, “Measuring and preventing COVID-19 using the SIR model and machine learning in smart health care,” Journal of Healthcare Engineering, vol. 2020, 12 pages, 2020.
[12]
AI Index Steering Committee, The AI Index 2021 Annual Report, Human-Centered AI Institute, Stanford University, Stanford, CA, USA, 2021.
[13]
M. Kikuchi, T. Hayashida, R. Watanuki, A. Nakashoji, Y. Kawai, and A. Nagayama, “Abstract p1-02-09: diagnostic system of breast ultrasound images using convolutional neural network,” Cancer Research, vol. 80, 2020.
[14]
S. Ekici and H. Jawzal, “Breast cancer diagnosis using thermography and convolutional neural networks,” Medical Hypotheses, vol. 137, 2020.
[15]
M. Toğaçar, B. Ergen, and Z. Cömert, “Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders,” Medical Hypotheses, vol. 135, 2020.
[16]
H. Yektaei, M. Manthouri, and F. Farivar, “Diagnosis of breast cancer using multiscale convolutional neural network,” Biomedical Engineering: Applications, Basis and Communications, vol. 31, no. 05, 2019.
[17]
R. Rouhi, M. Jafari, S. Kasaei, and P. Keshavarzian, “Benign and malignant breast tumors classification based on region growing and CNN segmentation,” Expert Systems with Applications, vol. 42, no. 3, pp. 990–1002, 2015.
[18]
M. Telegrafo, L. Rella, A. A. Stabile Ianora, G. Angelelli, and M. Moschetta, “Effect of background parenchymal enhancement on breast cancer detection with magnetic resonance imaging,” Diagnostic and Interventional Imaging, vol. 97, no. 3, pp. 315–320, 2016.
[19]
R. Guo, G. Lu, B. Qin, and B. Fei, “Ultrasound imaging technologies for breast cancer detection and management: a review,” Ultrasound in Medicine and Biology, vol. 34, 2017.
[20]
D. Khalilabad, Nastaran, and H. Hassanpour, “Employing image processing techniques for cancer detection using microarray images,” Computers in Biology and Medicine, vol. 81, no. 1, pp. 139–147, 2016.
[21]
N. I. R. Yassin, S. Omran, E. M. El Houby, H. Allam, and H. Allam, “Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review,” Computer Methods and Programs in Biomedicine, vol. 156, pp. 25–45, 2018.
[22]
M. Karabatak, “A new classifier for breast cancer detection based on Naïve Bayesian,” Measurement, vol. 72, pp. 32–36, 2015.
[23]
F. Wang, S. Zhang, and L. M. Henderson, “Adaptive decision-making of breast cancer mammography screening: a heuristic-based regression model,” Omega, vol. 76, pp. 70–84, 2018.
[24]
Patel, B. Charan, and G. R. Sinha, “Mammography feature analysis and mass detection in breast cancer images,” in Proceedings of the 2014 International Conference on Electronic Systems, pp. 474–478, Nagpur, India, January 2014.
[25]
A. K. Singh and B. Gupta, “A novel approach for breast cancer detection and segmentation in a mammogram,” Procedia Computer Science, vol. 54, pp. 676–682, 2015.
[26]
J. Tang, R. M. Rangayyan, J. N. Xu, I. El Naqa, and Y. Yang, “Computer-Aided detection and diagnosis of breast cancer with mammography: recent advances,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 236–251, 2009.
[27]
Y. Lee, “Preliminary evaluation of dual-head Compton camera with Si/CZT material for breast cancer detection: Monte Carlo simulation study,” Optik, vol. 10, 2019.
[28]
S. Khan, N. Islam, Z. Jan, I. Ud Din, and J. J. P. C. Rodrigues, “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.
[29]
P. Kaur, G. Singh, and P. Kaur, “Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification,” Informatics in Medicine Unlocked, vol. 16, 2019.
[30]
F. Martínez-Martínez, M. J. Rupérez-Moreno, M. Martínez-Sober, J. A. Solves-Llorens, D. Lorente, A. Serrano-López, S. Martínez-Sanchis, C. Monserrat, and J. Martín-Guerrero, “A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time,” Computers in Biology and Medicine, vol. 90, pp. 116–124, 2017.
[31]
M. R. Mohebian, H. R. Marateb, M. Mansourian, M. A. Mañanas, and F. Mokarian, “A hybrid computer-aided-diagnosis system for prediction of breast cancer recurrence (HPBCR) using optimized Ensemble learning,” Computational and Structural Biotechnology Journal, vol. 15, pp. 75–85, 2017.
[32]
M. Malathi, P. Sinthia, F. Farzana, and G. Aloy Anuja Mary, “Breast cancer detection using active contour and classification by deep belief network,” Materials Today: Proceedings, vol. 45, pp. 2721–2724, 2021.
[33]
M. Desai and M. Shah, “An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN),” Clinical eHealth, vol. 4, no. 1–11, pp. 1–11, 2021.
[34]
S. Chabert, J. S. Castro, L. Muñoz, P. Cox, R. Riveros, J. Vielma, G. Huerta, M. Querales, C. Saavedra, A. Veloz, and R. Salas, “Image quality assessment to emulate experts’ perception in lumbar MRI using machine learning,” Applied Sciences, vol. 11, no. 14, p. 6616, 2021.
[35]
T. Mahmood, J. Li, Y. Pei, and F. Akhtar, “An automated in-depth feature learning algorithm for breast abnormality prognosis and robust characterization from mammography images using deep transfer learning,” Biology, vol. 10, no. 9, p. 859, 2021.
[36]
M. A. Naji, S. E. Filali, K. Aarika, E. H. Benlahmar, R. A. Abdelouhahid, and O. Debauche, “Machine learning algorithms for breast cancer prediction and diagnosis,” Procedia Computer Science, vol. 191, pp. 487–492, 2021.
[37]
S. A. Alanazi, M. M. Kamruzzaman, M. N. Islam Sarker, M. Alruwaili, Y. Alhwaiti, N. Alshammari, and M. H. Siddiqi, “Boosting breast cancer detection using convolutional neural network,” Journal of Healthcare Engineering, vol. 2021, 11 pages, 2021.
[38]
D. A. Ragab, M. Sharkas, S. Marshall, and J. Ren, “Breast cancer detection using deep convolutional neural networks and support vector machines,” PeerJ, vol. 7, 2019.
[39]
S. Kaymak, A. Helwan, and D. Uzun, “Breast cancer image classification using artificial neural networks,” Procedia Computer Science, vol. 120, pp. 126–131, 2017.
[40]
U. Naseem, J. Rashid, L. Ali, J. Kim, Q. E. U. Haq, M. J. Awan, and M. Imran, “An automatic detection of breast cancer diagnosis and prognosis based on machine learning using Ensemble of classifiers,” IEEE Access, vol. 10, pp. 78242–78252, 2022.
[41]
T. Kavitha, P. P. Mathai, C. Karthikeyan, M. Ashok, R. Kohar, J. Avanija, and S. Neelakandan, “Deep learning based capsule neural network model for breast cancer diagnosis using mammogram images,” Interdisciplinary Sciences: Computational Life Sciences, vol. 14, no. 1, pp. 113–129, 2022.
[42]
V. Durga Prasad Jasti, K. Arumugam, M. Naved, H. Pallathadka, F. Sammy, A. Raghuvanshi, and K. Karthikeyan, “Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis,” Security and Communication Networks, vol. 2022, 7 pages, 2022.
[43]
S. Sadia, M. Rizwan, T. Reddy Gadekallu, A. Javed, M. Rahmani, K. Jawad, and S. Bhatia, “Bio-imaging-based machine learning algorithm for breast cancer detection,” Diagnostics, vol. 17, 2022.
[44]
M. Mangukiya, A. Vaghani, and M. Savani, “Breast cancer detection with machine learning,” International Journal for Research in Applied Science and Engineering Technology, vol. 10, no. 2, pp. 141–145, 2022.
[45]
T. Huang, G. J. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Transactions on Acoustics, Speech, & Signal Processing, vol. 27, no. 1, pp. 13–18, 1979.
[46]
J. W. Lee and S. H. Hong, “Bi-histogram equalization based on differential compression method for preserving the trend of natural mean brightness,” Journal of Broadcast Engineering, vol. 19, no. 4, pp. 453–467, 2014.
[47]
B. Alhadidi and M. H. Zobi, “Mammogram breast cancer detection using image processing fanction,” Information Technalogy Journal, vol. 12, 2007.
[48]
R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 6, pp. 610–621, 1973.
[49]
R. M. Haralick, “Statistical and structural approaches to texture,” Proceedings of the IEEE, vol. 67, pp. 786–804, 1979.
[50]
B. Yegnanarayana, Articial Neural Networks, Prentice-Hall, New Delhi, India, 1999.
[51]
A. I. Sharifi and K. Alizadeh, “Prediction of breast tumor malignancy using neural network and whale optimization algorithms (WOA),” Iranian Quarterly Journal of Breast Disease, vol. 12, no. 3, pp. 26–35, 2019.
[52]
F. J. Fernández-Ovies, E. S. Alférez-Baquero, E. J. de Andrés-Galiana, A. Cernea, Z. Fernández-Muñiz, and J. L. FernándezMartínez, “Detection of breast cancer using infrared thermography and deep neural networks,” in International Work-Conference on Bioinformatics and Biomedical Engineering, Springer, Berlin, Germany, 2019.
[53]
N. Croisard, M. Vasile, S. Kemble, and G. Radice, “Preliminary space mission design under uncertainty,” Acta Astronautica, vol. 66, no. 5-6, pp. 654–664, 2010.
[54]
J. C. Helton, “Uncertainty and sensitivity analysis in thePresence of stochastic and subjective uncertainty,” Journal of Statistical Computation and Simulation, vol. 57, no. 1-4, pp. 3–76, 1997.
[55]
A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” The Annals of Mathematical Statistics, vol. 38, pp. 325–339, 1967.
[56]
C. Fu, B. Hou, W. Chang, N. Feng, and S. Yang, “Comparison of evidential reasoning algorithm with linear combination in decision making,” International Journal of Fuzzy Systems, vol. 22, no. 2, pp. 686–711, 2020.
[57]
Z. G. Liu, Q. Pan, J. Dezert, and A. Martin, “Combination of classifiers with optimal weight based on evidential reasoning,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 3, pp. 1217–1230, 2018.
[58]
C. Fan, Y. Song, L. Lei, X. Wang, and S. Bai, “Evidence reasoning for temporal uncertain information based on relative reliability evaluation,” Expert Systems with Applications, vol. 113, pp. 264–276, 2018.
[59]
F. Xiao, “Generalization of Dempster-Shafer theory: a complex mass function,” Applied Intelligence, vol. 50, no. 10, pp. 3266–3275, 2020.
[60]
F. Xiao, “Generalized belief function in complex evidence theory,” Journal of Intelligent and Fuzzy Systems, vol. 38, no. 4, pp. 3665–3673, 2020.
[61]
A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” The Annals of Mathematical Statistics, vol. 38, no. 2, pp. 325–339, 1967.
[62]
G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, USA, 1976.
[63]
M. Zhou, X. B. Liu, Y. W. Chen, and J. B. Yang, “Evidential reasoning rule for MADM with both weights and reliabilities in group decision making,” Knowledge-Based Systems, vol. 143, pp. 142–161, 2018.
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Published: 01 January 2023
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