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Year 2020, Volume: 5 Issue: 1, 10 - 14, 30.06.2020

Abstract

References

  • [1] S. Khan, N. Islam, Z. Jan, I. U. Din, and J. J. 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. [2] A. C. Peterson and H. Uppal, "Method for predicting response to breast cancer therapeutic agents and method of treatment of breast cancer," ed: Google Patents, 2019. [3] Q. D. Buchlak et al., "Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review," Neurosurgical review, pp. 1-19, 2019. [4] A. Rodriguez-Ruiz et al., "Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists," JNCI: Journal of the National Cancer Institute, vol. 111, no. 9, pp. 916-922, 2019. [5] D. Dua and C. J. U. h. a. i. u. e. m. Graff, "UCI machine learning repository, 2017," vol. 37, 2019. [6] M. Hofmann and R. Klinkenberg, RapidMiner: Data mining use cases and business analytics applications. CRC Press, 2016. [7] G. O. TEMEL, S. ERDOĞAN, and H. ANKARALI, "Sınıflama Modelinin Performansını Değerlendirmede Yeniden Örnekleme Yöntemlerinin Kullanımı," Bilişim Teknolojileri Dergisi, vol. 5, no. 3, pp. 1-8, 2012. [8] I. Mierswa and R. Klinkenberg, "RapidMiner Studio Version 9.5," ed, 2019. [9] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: a cancer journal for clinicians, vol. 68, no. 6, pp. 394-424, 2018. [10] WHO. (2018). Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. Available: https://www.who.int/cancer/PRGlobocanFinal.pdf [11] N. ALPASLAN, "MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ," Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, vol. 7, no. 1, pp. 213-227, 2019. [12] V. Bajaj, M. Pawar, V. K. Meena, M. Kumar, A. Sengur, and Y. Guo, "Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition," Neural Computing and Applications, vol. 31, no. 8, pp. 3307-3315, 2019. [13] H. Kör, "Classification of Breast Cancer by Machine Learning Methods." [14] A. M. Abdel-Zaher and A. M. Eldeib, "Breast cancer classification using deep belief networks," Expert Systems with Applications, vol. 46, pp. 139-144, 2016. [15] A. Lee et al., "BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors," 2019. [16] A. Brédart et al., "Clinicians’ use of breast cancer risk assessment tools according to their perceived importance of breast cancer risk factors: an international survey," Journal of community genetics, vol. 10, no. 1, pp. 61-71, 2019. [17] S. Karadag Arli, A. B. Bakan, and G. Aslan, "Distribution of cervical and breast cancer risk factors in women and their screening behaviours," European journal of cancer care, vol. 28, no. 2, p. e12960, 2019.

Classification of Breast Cancer and Determination of Related Factors with Deep Learning Approach

Year 2020, Volume: 5 Issue: 1, 10 - 14, 30.06.2020

Abstract

Aim: In this study, it is aimed to classify breast cancer and identify related factors by applying deep learning method on open access to breast cancer dataset.
Materials and Methods: In this study, 11 variables related to open access to breast cancer dataset of 569 patients shared by the University of Wisconsin were used. The deep learning model for classifying breast cancer was established by a 10-fold cross-validation method. The performance of the model was evaluated with accuracy, sensitivity, specificity, positive/negative predictive values, F-score, and area under the curve (AUC). Factors associated with breast cancer were estimated from the deep learning model.
Results: Accuracy, specificity, AUC, sensitivity, positive predictive value, negative predictive value, and F-score values obtained from the model were 94.91%, 91.47%, 0.988, 96.90%, 95.42%, 95.14%, and 96.03%, respectively. In this study, when the effects of the variables in the dataset on breast cancer were evaluated, the three most important variables were obtained as area mean, concave points mean and symmetry mean, respectively.
Conclusion: The findings of this study showed that the deep learning model provided successful predictions for the classification of breast cancer. Also, unlike similar studies examining the same dataset, the importance values of cancer-related factors were estimated with the help of the model. In the following studies, breast cancer classification performances can give more successful predictions thanks to different deep learning architectures and ensemble learning approaches.

References

  • [1] S. Khan, N. Islam, Z. Jan, I. U. Din, and J. J. 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. [2] A. C. Peterson and H. Uppal, "Method for predicting response to breast cancer therapeutic agents and method of treatment of breast cancer," ed: Google Patents, 2019. [3] Q. D. Buchlak et al., "Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review," Neurosurgical review, pp. 1-19, 2019. [4] A. Rodriguez-Ruiz et al., "Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists," JNCI: Journal of the National Cancer Institute, vol. 111, no. 9, pp. 916-922, 2019. [5] D. Dua and C. J. U. h. a. i. u. e. m. Graff, "UCI machine learning repository, 2017," vol. 37, 2019. [6] M. Hofmann and R. Klinkenberg, RapidMiner: Data mining use cases and business analytics applications. CRC Press, 2016. [7] G. O. TEMEL, S. ERDOĞAN, and H. ANKARALI, "Sınıflama Modelinin Performansını Değerlendirmede Yeniden Örnekleme Yöntemlerinin Kullanımı," Bilişim Teknolojileri Dergisi, vol. 5, no. 3, pp. 1-8, 2012. [8] I. Mierswa and R. Klinkenberg, "RapidMiner Studio Version 9.5," ed, 2019. [9] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: a cancer journal for clinicians, vol. 68, no. 6, pp. 394-424, 2018. [10] WHO. (2018). Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. Available: https://www.who.int/cancer/PRGlobocanFinal.pdf [11] N. ALPASLAN, "MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ," Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, vol. 7, no. 1, pp. 213-227, 2019. [12] V. Bajaj, M. Pawar, V. K. Meena, M. Kumar, A. Sengur, and Y. Guo, "Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition," Neural Computing and Applications, vol. 31, no. 8, pp. 3307-3315, 2019. [13] H. Kör, "Classification of Breast Cancer by Machine Learning Methods." [14] A. M. Abdel-Zaher and A. M. Eldeib, "Breast cancer classification using deep belief networks," Expert Systems with Applications, vol. 46, pp. 139-144, 2016. [15] A. Lee et al., "BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors," 2019. [16] A. Brédart et al., "Clinicians’ use of breast cancer risk assessment tools according to their perceived importance of breast cancer risk factors: an international survey," Journal of community genetics, vol. 10, no. 1, pp. 61-71, 2019. [17] S. Karadag Arli, A. B. Bakan, and G. Aslan, "Distribution of cervical and breast cancer risk factors in women and their screening behaviours," European journal of cancer care, vol. 28, no. 2, p. e12960, 2019.
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Details

Primary Language English
Subjects Electrical Engineering
Journal Section Articles
Authors

Emek Güldoğan 0000-0002-5436-8164

Zeynep Tunç This is me

Cemil Çolak 0000-0001-5406-098X

Publication Date June 30, 2020
Published in Issue Year 2020 Volume: 5 Issue: 1

Cite

APA Güldoğan, E., Tunç, Z., & Çolak, C. (2020). Classification of Breast Cancer and Determination of Related Factors with Deep Learning Approach. The Journal of Cognitive Systems, 5(1), 10-14.