Araştırma Makalesi
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A Deep Feature Based Decision Support System for Breast Cancer Diagnosis

Yıl 2019, Cilt: 7 Sayı: 1, 213 - 227, 01.03.2019
https://doi.org/10.15317/Scitech.2019.193

Öz

The breast cancer is the second leading cause of cancer deaths among women
after lung cancer.  Early diagnosis is
quite significant with breast cancer treatment. Mammography is the most
commonly used imaging technique for the early detection of breast cancer.
Researches have been shown that the mortality rate can decrease by 30% for
women who have mammogram regularly over 50 years of age
(Jadoon et al. 2017). However, interpreting mammograms is often subjective.



In this study, an integrated system for automated detection,
classification, and content based retrieval of breast masses is presented.  In this manner, physician’s decisions on mass
were expressed by high-level deep features and low-level feature set. In
proposed framework, to extract low-level features, a graph based visual
saliency (GBVS) method is used for mass detection and the nonsubsampled
contourlet transform (NSCT) and eig(Hess)-HOG are used for feature extraction.
In addition, high-level convolutional neural network features have been used.
Then, two extreme learning machine (ELM) classifiers rely on the features
mentioned above is employed to predict category of test images. And outputs of
classifiers based on each feature were examined together to define the kind of
test image. The image retrieval and classification performances are evaluated
and compared on IRMA mammographic dataset by using both the precision-recall
(PR) and classification accuracies. Experimental results demonstrate the
effectiveness of the proposed system and the viability of a real-time clinical
application.

Kaynakça

  • Agrawal, Praful, Mayank Vatsa, and Richa Singh. 2014. “Saliency Based Mass Detection from Screening Mammograms.” Signal Processing 99. Elsevier BV: 29–47.
  • Alto, Hilary. 2007. “Errata: Content-Based Retrieval and Analysis of Mammographic Masses.” Journal of Electronic Imaging 16 (1): 019801. https://doi.org/10.1117/1.2713758.
  • “American Cancer Society Published Second Edition of Global Cancer Atlas.” 2015. Oncology Times 37 (1). Ovid Technologies (Wolters Kluwer Health): 34.
  • Bottou, Léon. 2010. “Large-Scale Machine Learning with Stochastic Gradient Descent.” In Proceedings of COMPSTAT’2010, 177–86. Heidelberg: Physica-Verlag HD.
  • Chougrad, Hiba, Hamid Zouaki, and Omar Alheyane. 2018. “Deep Convolutional Neural Networks for Breast Cancer Screening.” Computer Methods and Programs in Biomedicine 157 (April). Elsevier: 19–30. https://doi.org/10.1016/J.CMPB.2018.01.011.
  • Cunha, A L Da, J Zhou, and M N Do. 2006. “The Nonsubsampled Contourlet Transform: Theory, Design, and Applications.” IEEE Transactions on Image Processing 15 (10). Institute of Electrical and Electronics Engineers (IEEE): 3089–3101. https://doi.org/10.1109/tip.2006.877507.
  • Deserno, Thomas M, Michael Soiron, Júlia E E de Oliveira, and Arnaldo de A. Araújo. 2012. “Computer-Aided Diagnostics of Screening Mammography Using Content-Based Image Retrieval.” Medical Imaging 2012: Computer-Aided Diagnosis. SPIE-Intl Soc Optical Eng.
  • Do, M N, and M Vetterli. 2005. “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation.” IEEE Transactions on Image Processing 14 (12). Institute of Electrical and Electronics Engineers (IEEE): 2091–2106. https://doi.org/10.1109/tip.2005.859376.
  • Donahue, Jeff, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2013. “DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition,” October. http://arxiv.org/abs/1310.1531.
  • Dong, Min, Xiangyu Lu, Yide Ma, Yanan Guo, Yurun Ma, and Keju Wang. 2015. “An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.” Journal of Digital Imaging 28 (5). Springer Nature: 613–25. https://doi.org/10.1007/s10278-015-9778-4.
  • El-Naqa, I, Y Yang, N P Galatsanos, R M Nishikawa, and M N Wernick. 2004. “A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography.” IEEE Transactions on Medical Imaging 23 (10). Institute of Electrical and Electronics Engineers (IEEE): 1233–44.
  • Ganesan, Karthikeyan, U Rajendra Acharya, Chua Kuang Chua, Lim Choo Min, K Thomas Abraham, and Kwan-Hoong Ng. 2013. “Computer-Aided Breast Cancer Detection Using Mammograms: A Review.” IEEE Reviews in Biomedical Engineering 6. Institute of Electrical and Electronics Engineers (IEEE): 77–98.
  • Gedik, Nebi, Ayten Atasoy, and Yusuf Sevim. 2016. “Investigation of Wave Atom Transform by Using the Classification of Mammograms.” Applied Soft Computing 43: 546–52.
  • Hanbay, K., N. Alpaslan, M.F. Talu, D. Hanbay, A. Karci, and A.F. Kocamaz. 2015. “Continuous Rotation Invariant Features for Gradient-Based Texture Classification.” Computer Vision and Image Understanding 132. https://doi.org/10.1016/j.cviu.2014.10.004.
  • Jadoon, M. Mohsin, Qianni Zhang, Ihsan Ul Haq, Sharjeel Butt, and Adeel Jadoon. 2017. “Three-Class Mammogram Classification Based on Descriptive CNN Features.” BioMed Research International 2017. Hindawi Publishing Corporation: 1–11.
  • Jen, Chun-Chu, and Shyr-Shen Yu. 2015. “Automatic Detection of Abnormal Mammograms in Mammographic Images.” Expert Systems with Applications 42 (6). Elsevier BV: 3048–55.
  • Jiao, Zhicheng, Xinbo Gao, Ying Wang, and Jie Li. 2016. “A Deep Feature Based Framework for Breast Masses Classification.” Neurocomputing 197. Elsevier BV: 221–31.
  • Kinoshita, Sérgio Koodi, Paulo Mazzoncini de Azevedo-Marques, Roberto Rodrigues Pereira, Jośe Antônio Heisinger Rodrigues, and Rangaraj Mandayam Rangayyan. 2007. “Content-Based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns.” Journal of Digital Imaging 20 (2). Springer Nature: 172–90. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.
  • Liu, Xiaoming, and Jinshan Tang. 2014. “Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method.” IEEE Systems Journal 8 (3). Institute of Electrical and Electronics Engineers (IEEE): 910–20.
  • Meselhy Eltoukhy, Mohamed, Ibrahima Faye, and Brahim Belhaouari Samir. 2010. “A Comparison of Wavelet and Curvelet for Breast Cancer Diagnosis in Digital Mammogram.” Computers in Biology and Medicine 40 (4). Elsevier BV: 384–91.
  • Moura, Daniel C, and Miguel A Guevara López. 2013. “An Evaluation of Image Descriptors Combined with Clinical Data for Breast Cancer Diagnosis.” International Journal of Computer Assisted Radiology and Surgery 8 (4). Springer Nature: 561–74.
  • Mousa, Dana S AL, Claudia Mello-Thoms, Elaine A Ryan, Warwick B Lee, Mariusz W Pietrzyk, Warren M Reed, Robert Heard, et al. 2014. “Mammographic Density and Cancer Detection.” Academic Radiology 21 (11). Elsevier BV: 1377–85. http://dx.doi.org/10.1016/j.acra.2014.06.004.
  • Nelson, Heidi D. 2009. “Screening for Breast Cancer: An Update for the U.S. Preventive Services Task Force.” Annals of Internal Medicine 151 (10). American College of Physicians: 727.
  • Nishikawa, Robert M. 2007. “Current Status and Future Directions of Computer-Aided Diagnosis in Mammography.” Computerized Medical Imaging and Graphics 31 (4–5). Elsevier BV: 224–35.
  • Rangayyan, Rangaraj M, Fábio J Ayres, and J E Leo Desautels. 2007. “A Review of Computer-Aided Diagnosis of Breast Cancer: Toward the Detection of Subtle Signs.” Journal of the Franklin Institute 344 (3–4). Elsevier BV: 312–48. https://doi.org/10.1016/j.jfranklin.2006.09.003.
  • Roth, Holger R., Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, and Ronald M. Summers. 2016. “Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation.” IEEE Transactions on Medical Imaging 35 (5): 1170–81. https://doi.org/10.1109/TMI.2015.2482920.
  • Rouhi, Rahimeh, Mehdi Jafari, Shohreh Kasaei, and Peiman Keshavarzian. 2015. “Benign and Malignant Breast Tumors Classification Based on Region Growing and CNN Segmentation.” Expert Systems with Applications 42 (3). Elsevier BV: 990–1002.
  • Siegel, Rebecca L, Kimberly D Miller, and Ahmedin Jemal. 2015. “Cancer Statistics, 2015.” CA: A Cancer Journal for Clinicians 65 (1). American Cancer Society: 5–29. http://dx.doi.org/10.3322/caac.21254.
  • Sun, Wenqing, Tzu-Liang (Bill) Tseng, Jianying Zhang, and Wei Qian. 2017. “Enhancing Deep Convolutional Neural Network Scheme for Breast Cancer Diagnosis with Unlabeled Data.” Computerized Medical Imaging and Graphics 57 (April). Pergamon: 4–9.
  • Swiderski, Bartosz, Stanislaw Osowski, Jaroslaw Kurek, Michal Kruk, Iwona Lugowska, Piotr Rutkowski, and Walid Barhoumi. 2017. “Novel Methods of Image Description and Ensemble of Classifiers in Application to Mammogram Analysis.” Expert Systems with Applications 81: 67–78.
  • Tabár, László, Bedrich Vitak, Tony Hsiu-Hsi Chen, Amy Ming-Fang Yen, Anders Cohen, Tibor Tot, Sherry Yueh-Hsia Chiu, et al. 2011. “Swedish Two-County Trial: Impact of Mammographic Screening on Breast Cancer Mortality during 3 Decades.” Radiology 260 (3). Radiological Society of North America (RSNA): 658–63. http://dx.doi.org/10.1148/radiol.11110469.
  • Vedaldi, Andrea, and Brian Fulkerson. 2010. “Vlfeat: An Open and Portable Library of Computer Vision Algorithms.” In Proceedings of the International Conference on Multimedia - MM ’10, 1469. New York, New York, USA: ACM Press. http://dl.acm.org/citation.cfm?doid=1873951.1874249.
  • Wang, Ying, Jie Li, and Xinbo Gao. 2014. “Latent Feature Mining of Spatial and Marginal Characteristics for Mammographic Mass Classification.” Neurocomputing 144. Elsevier BV: 107–18.
  • Xie, Weiying, Yunsong Li, and Yide Ma. 2016. “Breast Mass Classification in Digital Mammography Based on Extreme Learning Machine.” Neurocomputing 173. Elsevier BV: 930–41.
  • Zhang, Wenlu, Rongjian Li, Houtao Deng, Li Wang, Weili Lin, Shuiwang Ji, Dinggang Shen, et al. 2013. “Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks.” Nature 35 (11). Institute of Electrical and Electronics Engineers (IEEE): 529–33. Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang,Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang . 2015. “Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation.” NeuroImage 108 (March). Academic Press: 214–24.
  • Zhang, Yu, Noriko Tomuro, Jacob Furst, and Daniela Stan Raicu. 2011. “Building an Ensemble System for Diagnosing Masses in Mammograms.” International Journal of Computer Assisted Radiology and Surgery 7 (2). Springer Nature: 323–29.
  • Zheng, Bin. 2009. “Computer-Aided Diagnosis in Mammography Using Content-Based Image Retrieval Approaches: Current Status and Future Perspectives.” Algorithms 2 (2). MDPI AG: 828–49.

MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ

Yıl 2019, Cilt: 7 Sayı: 1, 213 - 227, 01.03.2019
https://doi.org/10.15317/Scitech.2019.193

Öz

Meme kanseri, akciğer kanserinden sonra kadınlarda kanser ölümlerinin
ikinci önemli sebebidir. Erken tanı, meme kanseri tedavisinde oldukça
önemlidir. Mamografi, meme kanserinin erken teşhisinde en çok kullanılan
görüntüleme tekniğidir. Yapılan araştırmalar, 50 yaşın üstünde düzenli
mamografi çektirmenin kadınlar için ölüm oranını %30 oranında azaltabileceğini
göstermektedir. Ancak, mamogramların yorumlanması genellikle özneldir.



Bu
çalışmada, göğüs kitlelerinin otomatik tespiti, sınıflandırılması ve içerik
tabanlı erişimi için entegre bir sistem sunulmuştur. Bu kapsamda, hekimlerin
kitle hakkındaki kararları, üst düzey derin öznitelikler ve düşük seviye
öznitelik seti ile ifade edilmiştir. Önerilen sistemde düşük seviyeli
öznitelikleri elde etmek için, kitle tespitinde graf tabanlı görsel çıkıntı
yöntemi kullanılmış ve öznitelik çıkarımı için örneklemesiz contourlet dönüşümü
ve eig(Hess)-HOG yöntemleri kullanılmıştır. Ayrıca, yüksek seviyeli evrişimsel
sinir ağı öznitelikleri kullanılmıştır. Ardından, test görüntülerinin
kategorisini tahmin etmek için yukarıda bahsedilen özniteliklere dayalı iki
aşırı öğrenme makinesi (AÖM) sınıflandırıcısı kullanılmıştır. Farklı
özniteliklere dayalı sınıflandırıcıların sonuçları, test görüntülerinin türünü
belirlemek için analiz edilmiştir. Görüntü erişimi ve sınıflandırma
performansları, hem kesinlik-duyarlılık hem de sınıflandırma doğrulukları
kullanarak IRMA mammographic patches veri setinde değerlendirilip ve
karşılaştırılmıştır. Deneysel sonuçlar, önerilen sistemin etkililiğini ve
gerçek zamanlı klinik uygulamalardaki kullanılabilirliğini göstermektedir.

Kaynakça

  • Agrawal, Praful, Mayank Vatsa, and Richa Singh. 2014. “Saliency Based Mass Detection from Screening Mammograms.” Signal Processing 99. Elsevier BV: 29–47.
  • Alto, Hilary. 2007. “Errata: Content-Based Retrieval and Analysis of Mammographic Masses.” Journal of Electronic Imaging 16 (1): 019801. https://doi.org/10.1117/1.2713758.
  • “American Cancer Society Published Second Edition of Global Cancer Atlas.” 2015. Oncology Times 37 (1). Ovid Technologies (Wolters Kluwer Health): 34.
  • Bottou, Léon. 2010. “Large-Scale Machine Learning with Stochastic Gradient Descent.” In Proceedings of COMPSTAT’2010, 177–86. Heidelberg: Physica-Verlag HD.
  • Chougrad, Hiba, Hamid Zouaki, and Omar Alheyane. 2018. “Deep Convolutional Neural Networks for Breast Cancer Screening.” Computer Methods and Programs in Biomedicine 157 (April). Elsevier: 19–30. https://doi.org/10.1016/J.CMPB.2018.01.011.
  • Cunha, A L Da, J Zhou, and M N Do. 2006. “The Nonsubsampled Contourlet Transform: Theory, Design, and Applications.” IEEE Transactions on Image Processing 15 (10). Institute of Electrical and Electronics Engineers (IEEE): 3089–3101. https://doi.org/10.1109/tip.2006.877507.
  • Deserno, Thomas M, Michael Soiron, Júlia E E de Oliveira, and Arnaldo de A. Araújo. 2012. “Computer-Aided Diagnostics of Screening Mammography Using Content-Based Image Retrieval.” Medical Imaging 2012: Computer-Aided Diagnosis. SPIE-Intl Soc Optical Eng.
  • Do, M N, and M Vetterli. 2005. “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation.” IEEE Transactions on Image Processing 14 (12). Institute of Electrical and Electronics Engineers (IEEE): 2091–2106. https://doi.org/10.1109/tip.2005.859376.
  • Donahue, Jeff, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2013. “DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition,” October. http://arxiv.org/abs/1310.1531.
  • Dong, Min, Xiangyu Lu, Yide Ma, Yanan Guo, Yurun Ma, and Keju Wang. 2015. “An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.” Journal of Digital Imaging 28 (5). Springer Nature: 613–25. https://doi.org/10.1007/s10278-015-9778-4.
  • El-Naqa, I, Y Yang, N P Galatsanos, R M Nishikawa, and M N Wernick. 2004. “A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography.” IEEE Transactions on Medical Imaging 23 (10). Institute of Electrical and Electronics Engineers (IEEE): 1233–44.
  • Ganesan, Karthikeyan, U Rajendra Acharya, Chua Kuang Chua, Lim Choo Min, K Thomas Abraham, and Kwan-Hoong Ng. 2013. “Computer-Aided Breast Cancer Detection Using Mammograms: A Review.” IEEE Reviews in Biomedical Engineering 6. Institute of Electrical and Electronics Engineers (IEEE): 77–98.
  • Gedik, Nebi, Ayten Atasoy, and Yusuf Sevim. 2016. “Investigation of Wave Atom Transform by Using the Classification of Mammograms.” Applied Soft Computing 43: 546–52.
  • Hanbay, K., N. Alpaslan, M.F. Talu, D. Hanbay, A. Karci, and A.F. Kocamaz. 2015. “Continuous Rotation Invariant Features for Gradient-Based Texture Classification.” Computer Vision and Image Understanding 132. https://doi.org/10.1016/j.cviu.2014.10.004.
  • Jadoon, M. Mohsin, Qianni Zhang, Ihsan Ul Haq, Sharjeel Butt, and Adeel Jadoon. 2017. “Three-Class Mammogram Classification Based on Descriptive CNN Features.” BioMed Research International 2017. Hindawi Publishing Corporation: 1–11.
  • Jen, Chun-Chu, and Shyr-Shen Yu. 2015. “Automatic Detection of Abnormal Mammograms in Mammographic Images.” Expert Systems with Applications 42 (6). Elsevier BV: 3048–55.
  • Jiao, Zhicheng, Xinbo Gao, Ying Wang, and Jie Li. 2016. “A Deep Feature Based Framework for Breast Masses Classification.” Neurocomputing 197. Elsevier BV: 221–31.
  • Kinoshita, Sérgio Koodi, Paulo Mazzoncini de Azevedo-Marques, Roberto Rodrigues Pereira, Jośe Antônio Heisinger Rodrigues, and Rangaraj Mandayam Rangayyan. 2007. “Content-Based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns.” Journal of Digital Imaging 20 (2). Springer Nature: 172–90. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.
  • Liu, Xiaoming, and Jinshan Tang. 2014. “Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method.” IEEE Systems Journal 8 (3). Institute of Electrical and Electronics Engineers (IEEE): 910–20.
  • Meselhy Eltoukhy, Mohamed, Ibrahima Faye, and Brahim Belhaouari Samir. 2010. “A Comparison of Wavelet and Curvelet for Breast Cancer Diagnosis in Digital Mammogram.” Computers in Biology and Medicine 40 (4). Elsevier BV: 384–91.
  • Moura, Daniel C, and Miguel A Guevara López. 2013. “An Evaluation of Image Descriptors Combined with Clinical Data for Breast Cancer Diagnosis.” International Journal of Computer Assisted Radiology and Surgery 8 (4). Springer Nature: 561–74.
  • Mousa, Dana S AL, Claudia Mello-Thoms, Elaine A Ryan, Warwick B Lee, Mariusz W Pietrzyk, Warren M Reed, Robert Heard, et al. 2014. “Mammographic Density and Cancer Detection.” Academic Radiology 21 (11). Elsevier BV: 1377–85. http://dx.doi.org/10.1016/j.acra.2014.06.004.
  • Nelson, Heidi D. 2009. “Screening for Breast Cancer: An Update for the U.S. Preventive Services Task Force.” Annals of Internal Medicine 151 (10). American College of Physicians: 727.
  • Nishikawa, Robert M. 2007. “Current Status and Future Directions of Computer-Aided Diagnosis in Mammography.” Computerized Medical Imaging and Graphics 31 (4–5). Elsevier BV: 224–35.
  • Rangayyan, Rangaraj M, Fábio J Ayres, and J E Leo Desautels. 2007. “A Review of Computer-Aided Diagnosis of Breast Cancer: Toward the Detection of Subtle Signs.” Journal of the Franklin Institute 344 (3–4). Elsevier BV: 312–48. https://doi.org/10.1016/j.jfranklin.2006.09.003.
  • Roth, Holger R., Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, and Ronald M. Summers. 2016. “Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation.” IEEE Transactions on Medical Imaging 35 (5): 1170–81. https://doi.org/10.1109/TMI.2015.2482920.
  • Rouhi, Rahimeh, Mehdi Jafari, Shohreh Kasaei, and Peiman Keshavarzian. 2015. “Benign and Malignant Breast Tumors Classification Based on Region Growing and CNN Segmentation.” Expert Systems with Applications 42 (3). Elsevier BV: 990–1002.
  • Siegel, Rebecca L, Kimberly D Miller, and Ahmedin Jemal. 2015. “Cancer Statistics, 2015.” CA: A Cancer Journal for Clinicians 65 (1). American Cancer Society: 5–29. http://dx.doi.org/10.3322/caac.21254.
  • Sun, Wenqing, Tzu-Liang (Bill) Tseng, Jianying Zhang, and Wei Qian. 2017. “Enhancing Deep Convolutional Neural Network Scheme for Breast Cancer Diagnosis with Unlabeled Data.” Computerized Medical Imaging and Graphics 57 (April). Pergamon: 4–9.
  • Swiderski, Bartosz, Stanislaw Osowski, Jaroslaw Kurek, Michal Kruk, Iwona Lugowska, Piotr Rutkowski, and Walid Barhoumi. 2017. “Novel Methods of Image Description and Ensemble of Classifiers in Application to Mammogram Analysis.” Expert Systems with Applications 81: 67–78.
  • Tabár, László, Bedrich Vitak, Tony Hsiu-Hsi Chen, Amy Ming-Fang Yen, Anders Cohen, Tibor Tot, Sherry Yueh-Hsia Chiu, et al. 2011. “Swedish Two-County Trial: Impact of Mammographic Screening on Breast Cancer Mortality during 3 Decades.” Radiology 260 (3). Radiological Society of North America (RSNA): 658–63. http://dx.doi.org/10.1148/radiol.11110469.
  • Vedaldi, Andrea, and Brian Fulkerson. 2010. “Vlfeat: An Open and Portable Library of Computer Vision Algorithms.” In Proceedings of the International Conference on Multimedia - MM ’10, 1469. New York, New York, USA: ACM Press. http://dl.acm.org/citation.cfm?doid=1873951.1874249.
  • Wang, Ying, Jie Li, and Xinbo Gao. 2014. “Latent Feature Mining of Spatial and Marginal Characteristics for Mammographic Mass Classification.” Neurocomputing 144. Elsevier BV: 107–18.
  • Xie, Weiying, Yunsong Li, and Yide Ma. 2016. “Breast Mass Classification in Digital Mammography Based on Extreme Learning Machine.” Neurocomputing 173. Elsevier BV: 930–41.
  • Zhang, Wenlu, Rongjian Li, Houtao Deng, Li Wang, Weili Lin, Shuiwang Ji, Dinggang Shen, et al. 2013. “Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks.” Nature 35 (11). Institute of Electrical and Electronics Engineers (IEEE): 529–33. Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang,Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang . 2015. “Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation.” NeuroImage 108 (March). Academic Press: 214–24.
  • Zhang, Yu, Noriko Tomuro, Jacob Furst, and Daniela Stan Raicu. 2011. “Building an Ensemble System for Diagnosing Masses in Mammograms.” International Journal of Computer Assisted Radiology and Surgery 7 (2). Springer Nature: 323–29.
  • Zheng, Bin. 2009. “Computer-Aided Diagnosis in Mammography Using Content-Based Image Retrieval Approaches: Current Status and Future Perspectives.” Algorithms 2 (2). MDPI AG: 828–49.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Nuh Alpaslan

Yayımlanma Tarihi 1 Mart 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 7 Sayı: 1

Kaynak Göster

APA Alpaslan, N. (2019). MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 7(1), 213-227. https://doi.org/10.15317/Scitech.2019.193
AMA Alpaslan N. MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ. sujest. Mart 2019;7(1):213-227. doi:10.15317/Scitech.2019.193
Chicago Alpaslan, Nuh. “MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7, sy. 1 (Mart 2019): 213-27. https://doi.org/10.15317/Scitech.2019.193.
EndNote Alpaslan N (01 Mart 2019) MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7 1 213–227.
IEEE N. Alpaslan, “MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ”, sujest, c. 7, sy. 1, ss. 213–227, 2019, doi: 10.15317/Scitech.2019.193.
ISNAD Alpaslan, Nuh. “MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7/1 (Mart 2019), 213-227. https://doi.org/10.15317/Scitech.2019.193.
JAMA Alpaslan N. MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ. sujest. 2019;7:213–227.
MLA Alpaslan, Nuh. “MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, c. 7, sy. 1, 2019, ss. 213-27, doi:10.15317/Scitech.2019.193.
Vancouver Alpaslan N. MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ. sujest. 2019;7(1):213-27.

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