Research Article
BibTex RIS Cite

Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

Year 2020, Volume: 41 Issue: 4, 968 - 975, 29.12.2020

Esma Uzunhisarcıklı , Volkan Göreke , Vekil Sarı

https://doi.org/10.17776/csj.691683
Cited By: 2

Abstract

In this study, mass detection from breast ultrasonography images was realized using deep neural networks. Dataset is a collection of publicly available ultrasonography images which were classified by their biopsy results. A total of 153 breast ultrasonography images that contain 89 malign and 64 benign tumours were used. Image augmentation and deep neural network software was developed using Python 3,5 environment on Visual Studio Community 2017 IDE. A hybrid method including Keras ImageDataGenerator Class and image preprocessing techniques was introduced. Twenty images from both classes were randomly split from the dataset for testing after the network was designed. The network had a success rate of 100% at an epoch value of 70. The result of this study was compared with the result of another study that implemented type-2 fuzzy inference system with a success rate of 99,34%.
As a conclusion, it can be expressed that the deep neural networks are more successful than fuzzy inference systems in tumour detection from breast ultrasonography images. Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images.

Keywords

Breast cancer Ultrasonography CAD Deep neural networks

References

  • [1] Tanha J., Salarabadi H., Aznab M., Farahi A. and Zoberi M. Relationship among prognostic indices of breast cancer using classification techniques, Informatics in Medicine Unlocked, 18 (2020) 1-9.
  • [2] Pan H. B. The role of breast ultrasound in early cancer detection, Journal of Medical Ultrasound, 24 (4) (2016)138-141.
  • [3] Salomon L. J., Winer N., Bernard J. P. and Ville Y. A score‐based method for quality control of fetal images at routine second‐trimester ultrasound examination, Prenatal Diagnosis, 28 (9) (2008) 822-827.
  • [4] Yassin N. I., Omran S., El Houby E. M. and Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review, Computer Methods and Programs in Biomedicine, 156 (2018) 25-45.
  • [5] Göreke V., Uzunhisarcıklı E. and Öztoprak B. Mamogram görüntüde meme kanserine ait kitlelerin bilgisayar destekli tespiti için tip-2 bulanık çıkarım sistemi tasarımı. Tıptekno’16, Tıp Teknolojileri Kongresi, 27-29 Ekim 2016, 280-284.
  • [6] Mamdani, E. H. Application of fuzzy algorithms for control of simple dynamic plant, Proceedings of the Institution of Electrical Engineers, 121 (12) (1974) 1585-1588.
  • [7] Epelbaum, T. Deep learning: Technical introduction. (2017).
  • [8] Chen C. H., Lee Y. W., Huang Y. S., Lan W. R., Chang R. F., Tu C. Y., Chen C. Y. and Liao W. C. Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network, Computer Methods and Programs in Biomedicine, 177 (2019) 175-182.
  • [9] Zadeh L. A. Fuzzy logic and approximate reasoning, Synthese, 30 (3-4) (1975) 407-428.
  • [10] Wang C. A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making, Lehigh University, Mechanical Engineering and Mechanics, Candidacy for the Degree of Masters of Science, (2015) 199.
  • [11] Mendel J. M. Type-2 fuzzy sets and systems: an overview, IEEE Computational Intelligence Magazine, 2 (1) (2007) 20-29.
  • [12] Choi, B. I. and Rhee, F. C. H. Interval type-2 fuzzy membership function generation methods for pattern recognition, Information Sciences, 179 (13) (2009) 2102-2122.
  • [13] Akgun, A., Sezer, E. A., Nefeslioglu, H. A., Gokceoglu, C. and Pradhan, B. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm, Computers & Geosciences, 38 (1) (2012) 23-34.
  • [14] Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, 36 (4) (1980) 193-202.
  • [15] Kızrak M. A. and Bolat B. Derin öğrenme ile kalabalık analizi üzerine detaylı bir araştırma, Bilişim Teknolojileri Dergisi, 11 (3) (2018) 263-286.
  • [16] Şeker A., Diri B. and Balık, H. H. Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme, Gazi Mühendislik Bilimleri Dergisi, 3 (3) (2017) 47-64.
  • [17] Chollet F. Deep Learning with Python, USA:1st ed. Manning Publications Co, 2018.
  • [18] Zhang Z. and Sejdić E. Radiological images and machine learning: trends, perspectives, and prospects, Computers in Biology and Medicine, 108 (2019) 354-370.
  • [19] Félix G., Siller M. and Alvarez E. N. A fingerprinting indoor localization algorithm based deep learning. Eighth International Conference on Ubiquitous and Future Networks, 5-8 July 2016, 1006-1011.
  • [20] Géron A. Hands-On Machine Learning with Scikit-Learn and TensorFlow, USA: 1st ed.: O'Reilly Media, 2017.
  • [21] McCaffregy J. Keras Succinctly, USA: 1st ed.: Syncfusion Inc, 2018.
  • [22] SonoSkills and Hitachi Medical Systems Europe. Netherlands. Available at: https://www.ultrasoundcases.info/more/about-us. Retrieved February 20, 2020.
  • [23] Sajjad M., Khan S., Muhammad K., Wu W., Ullah A. and Baik, S. W. Multi-grade brain tumor classification using deep CNN with extensive data augmentation, Journal of Computational Science, 30 (2019) 174-182.
  • [24] Chougrad H., Zouaki H. and Alheyane O. Multi-label transfer learning for the early diagnosis of breast cancer, Neurocomputing, (2019) https://doi.org/10.1016/j.neucom.2019.01.112.
  • [25] Bengio Y., Goodfellow I. and Courville A. Deep learning, Book in preparation for MIT Press, 2015.
  • [26] Baratloo A., Hosseini M., Negida A. and El Ashal, G. Part 1: simple definition and calculation of accuracy, sensitivity and specificity, Emergency, 3 (2) (2015) 48-49.
  • [27] Miranda, G. H. B. and Felipe, J. C. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization, Computers in biology and medicine, 64 (2015) 334-346.
  • [28] Zhang, Q., Xiao, Y., Dai, W., Suo, J., Wang, C., Shi, J. and Zheng, H. Deep learning based classification of breast tumors with shear-wave elastography, Ultrasonics, 72 (2016) 150-157.
  • [29] Han, S., Meng, Z., Khan, A. S. and Tong, Y. Incremental boosting convolutional neural network for facial action unit recognition, Advances in Neural Information Processing Systems, (2016) 109-117.
  • [30] Byra, M., Galperin, M., Ojeda‐Fournier, H., Olson, L., O'Boyle, M., Comstock, C. and Andre, M. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Medical physics, 46 (2) (2019) 746-755.
  • [31] Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K. and Martí, R. Automated breast ultrasound lesions detection using convolutional neural networks, IEEE journal of biomedical and health informatics, 22 (4) (2017) 1218-1226.
  • [32] Mohammed, M. A., Al-Khateeb, B., Rashid, A. N., Ibrahim, D. A., Abd Ghani, M. K. and Mostafa, S. A. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images, Computers & Electrical Engineering, 70 (2018) 871-882.
  • [33] Keleş, A., Keleş, A. and Yavuz, U. Expert system based on neuro-fuzzy rules for diagnosis breast cancer, Expert systems with applications, 38 (5) (2011) 5719-5726.
  • [34] Huang, Q., Hu, B. and Zhang, F. Evolutionary optimized fuzzy reasoning with mined diagnostic patterns for classification of breast tumors in ultrasound, Information Sciences, 502 (2019) 525-536.
  • [35] Uzunhisarcikli E. and Goreke V. A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system, Sādhanā, 43 (9) (2018) 138.

Year 2020, Volume: 41 Issue: 4, 968 - 975, 29.12.2020

Esma Uzunhisarcıklı , Volkan Göreke , Vekil Sarı

https://doi.org/10.17776/csj.691683
Cited By: 2

Abstract

References

  • [1] Tanha J., Salarabadi H., Aznab M., Farahi A. and Zoberi M. Relationship among prognostic indices of breast cancer using classification techniques, Informatics in Medicine Unlocked, 18 (2020) 1-9.
  • [2] Pan H. B. The role of breast ultrasound in early cancer detection, Journal of Medical Ultrasound, 24 (4) (2016)138-141.
  • [3] Salomon L. J., Winer N., Bernard J. P. and Ville Y. A score‐based method for quality control of fetal images at routine second‐trimester ultrasound examination, Prenatal Diagnosis, 28 (9) (2008) 822-827.
  • [4] Yassin N. I., Omran S., El Houby E. M. and Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review, Computer Methods and Programs in Biomedicine, 156 (2018) 25-45.
  • [5] Göreke V., Uzunhisarcıklı E. and Öztoprak B. Mamogram görüntüde meme kanserine ait kitlelerin bilgisayar destekli tespiti için tip-2 bulanık çıkarım sistemi tasarımı. Tıptekno’16, Tıp Teknolojileri Kongresi, 27-29 Ekim 2016, 280-284.
  • [6] Mamdani, E. H. Application of fuzzy algorithms for control of simple dynamic plant, Proceedings of the Institution of Electrical Engineers, 121 (12) (1974) 1585-1588.
  • [7] Epelbaum, T. Deep learning: Technical introduction. (2017).
  • [8] Chen C. H., Lee Y. W., Huang Y. S., Lan W. R., Chang R. F., Tu C. Y., Chen C. Y. and Liao W. C. Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network, Computer Methods and Programs in Biomedicine, 177 (2019) 175-182.
  • [9] Zadeh L. A. Fuzzy logic and approximate reasoning, Synthese, 30 (3-4) (1975) 407-428.
  • [10] Wang C. A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making, Lehigh University, Mechanical Engineering and Mechanics, Candidacy for the Degree of Masters of Science, (2015) 199.
  • [11] Mendel J. M. Type-2 fuzzy sets and systems: an overview, IEEE Computational Intelligence Magazine, 2 (1) (2007) 20-29.
  • [12] Choi, B. I. and Rhee, F. C. H. Interval type-2 fuzzy membership function generation methods for pattern recognition, Information Sciences, 179 (13) (2009) 2102-2122.
  • [13] Akgun, A., Sezer, E. A., Nefeslioglu, H. A., Gokceoglu, C. and Pradhan, B. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm, Computers & Geosciences, 38 (1) (2012) 23-34.
  • [14] Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, 36 (4) (1980) 193-202.
  • [15] Kızrak M. A. and Bolat B. Derin öğrenme ile kalabalık analizi üzerine detaylı bir araştırma, Bilişim Teknolojileri Dergisi, 11 (3) (2018) 263-286.
  • [16] Şeker A., Diri B. and Balık, H. H. Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme, Gazi Mühendislik Bilimleri Dergisi, 3 (3) (2017) 47-64.
  • [17] Chollet F. Deep Learning with Python, USA:1st ed. Manning Publications Co, 2018.
  • [18] Zhang Z. and Sejdić E. Radiological images and machine learning: trends, perspectives, and prospects, Computers in Biology and Medicine, 108 (2019) 354-370.
  • [19] Félix G., Siller M. and Alvarez E. N. A fingerprinting indoor localization algorithm based deep learning. Eighth International Conference on Ubiquitous and Future Networks, 5-8 July 2016, 1006-1011.
  • [20] Géron A. Hands-On Machine Learning with Scikit-Learn and TensorFlow, USA: 1st ed.: O'Reilly Media, 2017.
  • [21] McCaffregy J. Keras Succinctly, USA: 1st ed.: Syncfusion Inc, 2018.
  • [22] SonoSkills and Hitachi Medical Systems Europe. Netherlands. Available at: https://www.ultrasoundcases.info/more/about-us. Retrieved February 20, 2020.
  • [23] Sajjad M., Khan S., Muhammad K., Wu W., Ullah A. and Baik, S. W. Multi-grade brain tumor classification using deep CNN with extensive data augmentation, Journal of Computational Science, 30 (2019) 174-182.
  • [24] Chougrad H., Zouaki H. and Alheyane O. Multi-label transfer learning for the early diagnosis of breast cancer, Neurocomputing, (2019) https://doi.org/10.1016/j.neucom.2019.01.112.
  • [25] Bengio Y., Goodfellow I. and Courville A. Deep learning, Book in preparation for MIT Press, 2015.
  • [26] Baratloo A., Hosseini M., Negida A. and El Ashal, G. Part 1: simple definition and calculation of accuracy, sensitivity and specificity, Emergency, 3 (2) (2015) 48-49.
  • [27] Miranda, G. H. B. and Felipe, J. C. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization, Computers in biology and medicine, 64 (2015) 334-346.
  • [28] Zhang, Q., Xiao, Y., Dai, W., Suo, J., Wang, C., Shi, J. and Zheng, H. Deep learning based classification of breast tumors with shear-wave elastography, Ultrasonics, 72 (2016) 150-157.
  • [29] Han, S., Meng, Z., Khan, A. S. and Tong, Y. Incremental boosting convolutional neural network for facial action unit recognition, Advances in Neural Information Processing Systems, (2016) 109-117.
  • [30] Byra, M., Galperin, M., Ojeda‐Fournier, H., Olson, L., O'Boyle, M., Comstock, C. and Andre, M. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Medical physics, 46 (2) (2019) 746-755.
  • [31] Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K. and Martí, R. Automated breast ultrasound lesions detection using convolutional neural networks, IEEE journal of biomedical and health informatics, 22 (4) (2017) 1218-1226.
  • [32] Mohammed, M. A., Al-Khateeb, B., Rashid, A. N., Ibrahim, D. A., Abd Ghani, M. K. and Mostafa, S. A. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images, Computers & Electrical Engineering, 70 (2018) 871-882.
  • [33] Keleş, A., Keleş, A. and Yavuz, U. Expert system based on neuro-fuzzy rules for diagnosis breast cancer, Expert systems with applications, 38 (5) (2011) 5719-5726.
  • [34] Huang, Q., Hu, B. and Zhang, F. Evolutionary optimized fuzzy reasoning with mined diagnostic patterns for classification of breast tumors in ultrasound, Information Sciences, 502 (2019) 525-536.
  • [35] Uzunhisarcikli E. and Goreke V. A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system, Sādhanā, 43 (9) (2018) 138.
There are 35 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Engineering Sciences
Authors

Esma Uzunhisarcıklı 0000-0003-2821-4177

Volkan Göreke 0000-0002-2418-8373

Vekil Sarı 0000-0001-5963-0179

Publication Date December 29, 2020
Submission Date February 20, 2020
Acceptance Date November 29, 2020
Published in Issue Year 2020Volume: 41 Issue: 4

Cite

APA Uzunhisarcıklı, E., Göreke, V., & Sarı, V. (2020). Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images. Cumhuriyet Science Journal, 41(4), 968-975. https://doi.org/10.17776/csj.691683

Cited By

A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment
Applied Mathematics and Computation
https://doi.org/10.1016/j.amc.2022.127038
A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings
Applied Soft Computing
Volkan Göreke
https://doi.org/10.1016/j.asoc.2021.107329