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DOI: doi.org/10.36287/setsci
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SETSCI - Volume 4(5) (2019)
HORA2019 - International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ürgüp, Turkey, Jul 05, 2019

Classification of Breast Histopathology Images with Deep Learning Methods (HORA2019_19)
Meral Karakurt1, İsmail İşeri2*
1Ondokuz Mayıs University, Samsun, Turkey
2Ondokuz Mayıs University, Samsun, Turkey
* Corresponding author: ismail.iseri@omu.edu.tr
Published Date: 2019-10-12   |   Page (s): 87-98   |    35     7
https://doi.org/10.36287/setsci.4.5.019

ABSTRACT Cancer is the second most common cause of human death. Nowadays, the rate of people getting cancer is increasing.Breast cancer is the most common type of cancer among women. The exact causes of the disease are not known. Medical images are vital in detecting diseases that exist in the bodies of living things or in the early diagnosis of diseases that may occur. Particularly used for cancer detection, the pathology images are a type of medical image which is stained into a chemical material of a piece taken from the patient and scanned with special machines and transferred to computer. Many machine learning and artificial intelligence methods are used for the analysis of medical images. With these methods, significant information is extracted from the images to detect or predict abnormalities. In recent years, deep learning, which is one of the methods of artificial intelligence, has shown significant success in analyzing the pathological images of medical images. Deep learning architectures perform feature extraction in machine learning studies with the convolution layers within the architecture. Deep learning algorithms used for image classification, object recognition and segmentation have become one of the most preferred methods for the analysis of pathology images. In this thesis, an architecture has been developed by using CNN which is the most successful deep learning method. Of the 60000 breast pathology images, 50000 were devoted to training and 10000 for testing. A classification was performed on the NVIDIA Tesla K80 GPU accelerator using the Keras library and Python programming language and 0.8824 accuracy value, 0.0806 MSE value and 0.3363 MAE value was obtained. Kanser, insan ölümlerine sebep olan hastalıklar arasında ikinci sırada gelmektedir. Günümüzde insanların kansere yakalanma oranları giderek artmaktadır. Kanser; kötü beslenme, sigara ve alkol kullanımı, uzun süre güneş ışığına maruz kalma gibi birçok nedenle ortaya çıkmaktadır. Meme kanseri, kadınlar arasında en çok görülen kanser türüdür. Medikal görüntüler, canlıların vücutlarında var olan hastalıkların tespitinde veya meydana gelebilecek olan hastalıkların erken tanısında hayati öneme sahiptir. Özellikle kanser tespiti yapmak amacıyla kullanılan patoloji görüntüleri, hastadan alınan bir parçanın çeşitli kimyasal maddelere batırılıp özel makinelerle taranarak bilgisayar ortamına aktarılan medikal görüntü çeşididir. Medikal görüntülerin analiz edilmesi için birçok makine öğrenmesi ve yapay zeka yöntemleri kullanılmaktadır. Bu yöntemlerle görüntülerden anlamlı bilgiler çıkarılarak anormalliklerin tespit edilmesi veya tahmin edilmesi işlemleri yapılmaktadır. Son yıllarda yapay zeka yöntemlerinden birisi olan derin öğrenme, medikal görüntülerden olan pataloji görüntülerinin analiz edilmesinde önemli başarılar göstermektedir. Derin öğrenme mimarileri, makine öğrenimi çalışmalarındaki özellik çıkarım işlemini mimari içerisinde yer alan evrişim katmanları ile yapmaktadır. Görüntü sınıflandırma, nesne tanıma, segmentasyon gibi işlemler için kullanılan derin öğrenme algoritmaları, pataloji görüntülerinin analizi için en çok tercih edilen yöntemlerden biri haline gelmiştir. Bu tez çalışmasında, en başarılı derin öğrenme yöntemi olan evrişimel sinir ağları (CNN) kullanılarak bir mimari geliştirilmiştir. 60000 tane meme patoloji görüntüsünden oluşan bir veri setinin 50000 tanesi eğitim ve 10000 tanesi test için ayrılmıştır. NVIDIA Tesla K80 GPU hızlandırıcı üzerinde Keras kütüphanesi ve Python programlama dili kullanılarak bir sınıflandırma işlemi yapılmıştır ve 0.8824 oranında doğruluk değeri, 0.0806 MSE değeri ve 0.3363 MAE değeri elde edilmiştir.
KEYWORDS Breast Cancer, Pathology Images, Deep Learning, CNN, Keras, Classification
REFERENCES Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L. and Erickson, B. J. 2017. Deep learning for brain MRI segmentation: state of the art and future directions. Journal of digital imaging, 30:4, 449-459.

Alhussein, M. and Muhammad, G. 2018. Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework. IEEE Access, 6, 41034-41041.

Alipanahi, B., Delong, A., Weirauch, M. T. and Frey, B. J. 2015. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology, 33:8, 831.

Anonim. 1994. Yapay Sinir Ağları. Retrieved from https://www.coursehero.com/file/26933362/cinsdikici-neuralnet-girispdf/. (Erişim tarihi: 15.01.2018).

Anonim. 2008. Biyomedikal Cihaz Teknolojileri. Mesleki Eğitim ve Öğretim Sisteminin Güçlendirilmesi Projesi (MEGEP), Milli Eğitim Bakanlığı, Ankara.

Anonim. 2017. Görüntü İşleme (Image Processing) Nedir?.http://blog.udentify.co/04/2017/goruntu-isleme-nedir/ (Erişim tarihi: 10.06.2019).

Anonim. 2019. Gradyan İniş Optimizasyon Algoritmalarına Genel Bakış. https://devhunteryz.wordpress.com/2019/06/04/gradyan-inisoptimizasyon-algoritmalarina-genel-bakis/. (Erişim tarihi: 01.07.2019).

Atasoy, H. 2011. Gradyan ve Gradyan İniş. http://www.atasoyweb.net/Gradyan-Ve-Gradyan-Inis. (Erişim tarihi:10.01.2018).

Aytan, A.E., Öztürk, Y. ve Örgev, E.K. 1993. Görüntü İşleme. İ. Ü. Diş Hekimliği Fakültesi Dergisi. 27:4, 273-277.

Banu, M. S. and Nallaperumal, K. (2010). Analysis of Color Feature Extraction Techniques for Pathology Image Retrieval System. Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on, IEEE, 1-7.

Basavanhally, A. 2010. Automated Image-Based Detection and Grading of Lymphocytic İnfiltration in Breast Cancer Histopathology. Rutgers University-Graduate School-New Brunswick, New Brunswick, New Jersey.

Basavanhally, A., Yu, E., Xu, J., Ganesan, S., Feldman, M., Tomaszewski, J. and Madabhushi, A. (2011). Incorporating Domain Knowledge for Tubule Detection in Breast Histopathology Using O'Callaghan Neighborhoods. Medical Imaging 2011: Computer-Aided Diagnosis, International Society for Optics and Photonics, 1-13.

Bejnordi, B. E., Veta, M., Van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., Van Der Laak, J. A., Hermsen, M., Manson, Q. F. and Balkenhol, M. 2017. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women with Breast Cancer. Jama, 318:22, 2199-2210.

Bektaş, B. ve Babur, S. (2016). Makine Öğrenmesi Teknikleri Kullanılarak Meme Kanseri Teşhisinin Performans Değerlendirmesi Machine Learning Based Performance Development for Diagnosis of Breast Cancer.

Belsare, A. and Mushrif, M. 2012. Histopathological Image Analysis Using Image Processing Techniques: An Overview. Signal & Image Processing: An International Journal (SIPIJ), 3:4, 23-33.

Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X. and Xie, Z. 2018. Deep Learning and Its Applications in Biomedicine. Genomics, proteomics & bioinformatics, 16, 17-32.

Carrio, A., Sampedro, C., Rodriguez-Ramos, A. and Campoy, P. 2017. A review of deep learning methods and applications for unmanned aerial vehicles. Journal of Sensors, 2017.

Cengil, E. and Çınar, A. 2016. A New Approach for Image Classification: Convolutional Neural Network. European Journal of Technic, 6:2.

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. and Blaschke, T. 2018. The rise of deep learning in drug discovery. Drug discovery today.

Chen, H., Zhang, Y., Zhang, W., Liao, P., Li, K., Zhou, J. and Wang, G. 2017a. Low-dose CT via convolutional neural network. Biomedical optics express, 8:2, 679-694.

Chen, L., Bentley, P. and Rueckert, D. 2017b. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage: Clinical, 15, 633-643.

Chotitham, S., Wongwanich, S. and Wiratchai, N. 2014. Deep Learning and its Effects on Achievement. Procedia - Social and Behavioral Sciences, 116, 3313-3316.

Cireşan, D. C., Giusti, A., Gambardella, L. M. and Schmidhuber, J. (2013). Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, 411-418.

Cosatto, E., Miller, M., Graf, H. P. and Meyer, J. S. (2008). Grading Nuclear Pleomorphism on Histological Micrographs. Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, IEEE, 1-4.

Çayıroğlu, İ. 2018. Görüntü İşleme. Karabük Üniversitesi, Mühendislik Fakültesi. http://www.ibrahimcayiroglu.com/Dokumanlar/GoruntuIsleme/Goruntu_Isleme_Ders_Notlari-1.Hafta.pdf (Erişim tarihi: 15.02.2019).

Danaee, P., Ghaeini, R. and Hendrix, D. A. (2017). A deep learning approach for cancer detection and relevant gene identification. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017, World Scientific, 219-229.

Das, D. K. and Dutta, P. K. 2019. Efficient Automated Detection of Mitotic Cells From Breast Histological Images Using Deep Convolution Neutral Network with Wavelet Decomposed Patches. Computers in biology and medicine, 104, 29-42.

Davy, A., Havaei, M., Warde-Farley, D., Biard, A., Tran, L., Jodoin, P.-M., Courville, A., Larochelle, H., Pal, C. and Bengio, Y. 2014. Brain Tumor Segmentation with Deep Neural Networks. 1-5.

Demirel, S. O. 2008. Görüntü işleme teknikleri ve medikal uygulamaları. Ege Üniversitesi

Deniz, C. M., Xiang, S., Hallyburton, R. S., Welbeck, A., Babb, J. S., Honig, S., Cho, K. and Chang, G. 2018. Segmentation of the proximal femur from MR images using deep convolutional neural networks. Scientific reports, 8:1, 16485.

Dinsmore, C. 2014. Survey of Neural Networks in Digital Pathology and Pathology Workflow. Thesis, DePaul University Department of Computing and Digital Media 6, Chicago, IL.

Doolan, C., Colgan, O. and Heffner, S. 2018. Digital Pathology. https://www.leicabiosystems.com/pathologyleaders/digitalpathology/ (Erişim tarihi: 10.01.2019).

Ekmekji, A. 2016. Convolutional Neural Networks for Age and Gender Classification. Technical Report, 1:7. Stanford University, USA.

Ertosun, M. G. and Rubin, D. L. (2015). Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks. AMIA Annual Symposium Proceedings, American Medical Informatics Association, 1899.

Fakoor, R., Ladhak, F., Nazi, A. and Huber, M. (2013). Using deep learning to enhance cancer diagnosis and classification. Proceedings of the International Conference on Machine Learning, ACM New York, USA.

Fang, S.-H., Tsao, Y., Hsiao, M.-J., Chen, J.-Y., Lai, Y.-H., Lin, F.-C. and Wang, C.-T. 2018. Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach. Journal of Voice.

Fatakdawala, H., Xu, J., Basavanhally, A., Bhanot, G., Ganesan, S., Feldman, M., Tomaszewski, J. E. and Madabhushi, A. 2010.

Expectation–Maximization-Driven Geodesic Active Contour with Overlap Resolution (Emagacor): Application to Lymphocyte Segmentation on Breast Cancer Histopathology. IEEE Transactions on Biomedical Engineering, 57:7, 1676-1689.

Fausett, L. V. 1994. Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall Englewood Cliffs, 3-88,

Fu, X., Liu, T., Xiong, Z., Smaill, B. H., Stiles, M. K. and Zhao, J. 2018. Segmentation of Histological Images and Fibrosis Identification with a Convolutional Neural Network. Computers in biology and medicine, 98, 147-158.

Fukushima, K. and Miyake, S. 1982. Competition and cooperation in neural nets. Springer, 267-285.

Gandomkar, Z., Brennan, P. C. and Mello-Thoms, C. 2018. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artificial intelligence in medicine.

Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., Shin, H.-C., Roth, H., Papadakis, G. Z., Depeursinge, A. and Summers, R. M. 2018. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6:1, 1-6.

Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T. and Hu, Y. 2018. NiftyNet: a deep-learning platform for medical imaging. Computer methods and programs in biomedicine, 158, 113-122.

Glotsos, D., Spyridonos, P., Cavouras, D., Ravazoula, P., Dadioti, P.-A. and Nikiforidis, G. 2004. Automated segmentation of routinely hematoxylin-eosin-stained microscopic images by combining support vector machine clustering and active contour models. Analytical and quantitative cytology and histology, 26:6,
331-340.

Golatkar, A., Anand, D. and Sethi, A. (2018). Classification of Breast Cancer Histology Using Deep Learning. International Conference Image Analysis and Recognition, Springer, 837-844.

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T. and Cuadros, J. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316:22, 2402-2410.

Güzeldereli, E. A., Doğan, F. ve Çetin, Ö. 2013. Medikal Görüntü Içerisine Tıbbi Bilgilerin Gömülmesi İçin Yeni Bir Yaklaşım. Sakarya University Journal of Science, 17:2, 277-286.

Harorlı, D. H. ve Harorlı, O. T. 2012. Diş Hekimliğinde Görüntü Arşivleme ve İletişim Sistemleri. Atatürk Üniversitesi Diş Hekimliği Fakültesi Dergisi, 2012:3.

Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M. and Larochelle, H. 2017. Brain Tumor Segmentation with Deep Neural Networks. Medical image analysis, 35, 18-31.

Hebb, D. O. 1949. The Organization of Behavior. John What & Sons. Inc, 17-78, United States of America.

Hinton, G. E. (2007). Boltzmann Machines. Retrieved from Canada: https://www.cs.toronto.edu/~hinton/csc321/readings/boltz321.pdf. (Erişim tarihi: 10.01.2018).

Hinton, G. E., Osindero, S. and Teh, Y.-W. 2006. A fast learning algorithm for deep belief nets. Neural computation, 18:7, 1527-1554.

Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks (0036-8075).

Hinton, G.E., Srivastava, N. and Swerky, K. 2012. Neural Networks for Machine Learning. Lecture 6a Overview of mini-batch gradient descent .
https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf (Erişim tarihi: 01.06.2019).

Holzinger, A., Malle, B., Kieseberg, P., Roth, P. M., Müller, H., Reihs, R. and Zatloukal, K. 2017. Towards Integrative Machine Learning and Knowledge Extraction, 13-50.

Hopfield, J. J. 1982. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79:8, 2554-2558.

İnik, Ö. ve Ülker, E. 2017. Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Gaziosmanpasa Journal of Scientific Research, 6, 85-104.

Isin, A. and Ozdalili, S. 2017. Cardiac arrhythmia detection using deep learning. Procedia Computer Science, 120, 268-275.

Işık, G. ve Artuner, H. 2016. Radyo Sinyallerinin Derin Öğrenme Sinir Ağları ile Tanınması Recognition of Radio Signals with Deep Learning Neural Networks.

Işın, A., Direkoğlu, C. and Şah, M. 2016. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 102, 317-324.

Ivakhnenko, A. G. and Lapa, V. G. 1965. Cybernetic predicting devices. CCM Information Corporation.

Janowczyk, A. and Madabhushi, A. 2016. Deep Learning for Digital Pathology Image Analysis: A Comprehensive Tutorial with Selected Use Cases. Journal of pathology informatics, 7.

Kaplan, M. 2019. Patoloji Nedir? Patolog Ne İş Yapar? Patoloji Testleri ve Raporu. https://www.medikalakademi.com.tr/patoloji-nedir-patolog-neis-yapar-patoloji-testleri-ve-raporu/ (Erişim tarihi: 28.06.2019).

Karakoç, M. 2012. Görüntü İşleme, Teknolojiler ve Uygulamaları. Ege Üniversitesi. https://docplayer.biz.tr/883704-Goruntu-isleme-teknolojiler-ve-uygulamalari.html (Erişim tarihi: 28.06.2019).

Kaya, T. 2017. Radyografinin Temel Prensipleri ve Radyografik Yorumda Temel İlkeler.

Kaynar, O., Aydın, Z. ve Görmez, Y. 2017. Sentiment Analizinde Öznitelik Düşürme Yöntemlerinin Oto Kodlayıcılı Derin Öğrenme Makinaları ile Karşılaştırılması. Bilişim Teknolojileri Dergisi, 10:3, 319-326.

Kaynar, O., Görmez, Y. ve Işık, Y. E. (2016). Oto Kodlayici Tabanli Derİn Öğrenme Makİnalari İle Spam Tespİtİ. 3. Uluslararası Yönetim Bilişim Sistemleri Konferansı.

Keskenler, M. F. ve Keskenler, E. F. 2017. Geçmişten Günümüze Yapay Sinir Ağları ve Tarihçesi. Takvim-i Vekayi, 5:2, 8-18.

Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O. and Hajirasouliha, I. 2018. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine, 27, 317-328.

Kızrak, A. 2019. Derin Öğrenme için Aktivasyon Fonksiyonlarının Karşılaştırılması. https://medium.com/@ayyucekizrak/derin-%C3%B6%C4%9Frenme-i%C3%A7in-aktivasyonfonksiyonlar%C4%B1n%C4%B1nkar%C5%9F%C4%B1la%C5%9Ft%C4%B1r%C4%B1lmas%C4%B1-cee17fd1d9cd. (Erişim tarihi: 11.04.2019)

Kingma, D.P. and Ba, J.L. (2015). Adam: A Method For Stochastic Optimization. Conference paper at ICLR 2015, 7-9 May, 1-11, USA.

Kohl, M., Walz, C., Ludwig, F., Braunewell, S. and Baust, M. (2018). Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks. International Conference Image Analysis and Recognition, Springer, 903-913.

Kohonen, T. 1982. Self-organized formation of topologically correct feature maps. Biological cybernetics, 43:1, 59-69.

Kolachalama, V. B., Singh, P., Lin, C. Q., Mun, D., Belghasem, M. E., Henderson, J. M., Francis, J. M., Salant, D. J. and Chitalia, V. C. 2018. Association of pathological fibrosis with renal survival using deep neural networks. Kidney international reports, 3:2, 464-475.

Komura, D. and Ishikawa, S. 2018. Machine Learning Methods for Histopathological Image Analysis. Computtational and Structural Biotechnology Journal, 16, 34-42.

Koyun, A. ve Afşin, E. Derin Öğrenme ile İki Boyutlu Optik Karakter Tanıma. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 10:1, 11-14.

Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 1097-1105.

Kvam, J. and Kongsro, J. 2017. In vivo prediction of intramuscular fat using ultrasound and deep learning. Computers and Electronics in Agriculture, 142, 521-523.

LeCun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning. nature, 521:7553, 436-442.

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. and Jackel, L. D. 1989. Backpropagation applied to handwritten zip code recognition. Neural computation, 1:4, 541-551.

LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86:11, 2278-2324.

Lee, C. S., Tyring, A. J., Deruyter, N. P., Wu, Y., Rokem, A. and Lee, A. Y. 2017. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomedical optics express, 8:7, 3440-3448.

Li, H., Lin, Z., Shen, X., Brandt, J. and Hua, G. (2015). A convolutional neural network cascade for face detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5325-5334.

Lippmann, R. P. 1989. Pattern classification using neural networks. IEEE communications magazine, 27:11, 47-50.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B. and Sánchez, C. I. 2017. A Survey on Deep Learning in Medical Image Analysis. Medical image analysis, 42, 60-88.

Lo, S.-C. B., Chan, H.-P., Lin, J.-S., Li, H., Freedman, M. T. and Mun, S. K. 1995. Artificial convolution neural network for medical image pattern recognition. Neural networks, 8:7-8, 1201-1214.

Madabhushi, A. and Lee, G. 2016. Image analysis and machine learning in digital pathology: Challenges and opportunities: Elsevier.

Maninis, K.-K., Pont-Tuset, J., Arbeláez, P. and Van Gool, L. (2016). Deep retinal image understanding. International Conference on Medical Image Computing and ComputerAssisted Intervention, Springer, 140-148.

McCulloch, W. S. and Pitts, W. 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5:4, 115-133.

Montavon, G., Samek, W. and Müller, K.-R. 2018. Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1-15.

Motlagh, N. H., Jannesary, M., Aboulkheyr, H., Khosravi, P., Elemento, O., Totonchi, M. and Hajirasouliha, I. 2018. Breast Cancer Histopathological Image Classification: A Deep Learning Approach. bioRxiv, 242818.

Murthy, V., Hou, L., Samaras, D., Kurc, T. M. and Saltz, J. H. (2017). Center-Focusing Multi-Task CNN with Injected Features for Classification of Glioma Nuclear Images. Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on, IEEE, 834-841.

Nedzved, A., Belotserkovsky, A., Lehmann, T. and Ablameyko, S. (2007). Morphometrical Feature Extraction on Color Histological Images for Oncological Diagnostics. 5th International Conference on Biomedical Engineering, 379-384.

Nirschl, J. J., Janowczyk, A., Peyster, E. G., Frank, R., Margulies, K. B., Feldman, M. D. and Madabhushi, A. 2017. Deep Learning for Medical Image Analysis. Elsevier, 179-195.

Ortakaya, P. 2014. NVIDIA Veri Analitiği ve Bilimsel Hesaplama için Dünyanın En İyi Hızlandırıcısını Duyurdu.https://www.nvidia.com.tr/object/tesla-k80-dual-gpu-acceleratoroct-14-2014-tr.html. (Erişim tarihi: 11.09.2017).

Pantanowitz, L. (2010). Digital images and the future of digital pathology. Journal of pathology informatics, Omaha, Nebraska.

Paramanandam, M., O’Byrne, M., Ghosh, B., Mammen, J. J., Manipadam, M. T., Thamburaj, R. and Pakrashi, V. 2016. Automated segmentation of nuclei in breast cancer histopathology images. PloS one, 11:9, e0162053.

Pişkin, M. 2017. TensorFlow ile Sınıflandırıcı Eğitimi ve Görüntü Sınıflandırma. http://mesutpiskin.com/blog/tensorflowile-siniflandirici-egitimi-ve-goruntu-siniflandirma.html. (Erişim tarihi: 04.01.2018).

Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S. and Thoma, G. 2018. Image analysis and machine learning for detecting malaria. Translational Research, 194, 36-55.

Qu, J., Hiruta, N., Terai, K., Nosato, H., Murakawa, M. and Sakanashi, H. 2018. Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks. Journal of Healthcare Engineering, 2018, 1-11.

Rani, R. U. and Amsini, P. 2018. Image Processing Techniques Used In Digital Pathology Imaging: An Overview International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5:1, 1-4.

Rende, F. Ş., Bütün, G. ve Karahan, Ş. 2017. Derin Öğrenme Algoritmalarında Model Testleri: Derin Testler. 10. Ulusal Yazılım Mühendisliği Sempozyumu, 24-26 Ekim, sayfa:54-59, Çanakkale, Türkiye.

Rosenblatt, F. 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65:6, 386.

Roy, K., Banik, D., Bhattacharjee, D. and Nasipuri, M. 2019. Patch-Based System for Classification of Breast Histology Images Using Deep Learning. Computerized Medical Imaging and Graphics, 71, 90-103.

Ruder, S. 2017. An Overview of Gradient Descent Optimization Algorithms. arXiv preprint arXiv:1609.04747.

Rumelhart, D. E., Hinton, G. E. and Williams, R. J. 1986. Learning representations by back-propagating errors. nature, 323:6088, 533-535.

Sabeena, B. K., Nair, M. S. and Bindu, G. 2018. Automatic Mitosis Detection in Breast Histopathology Images Using Convolutional Neural Network Based Deep Transfer Learning. Biocybernetics and Biomedical Engineering.

Saha, M., Chakraborty, C. and Racoceanu, D. 2018. Efficient Deep Learning Model for Mitosis Detection Using Breast Histopathology Images. Computerized Medical Imaging and Graphics, 64, 29-40.

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T. and Batiste, R. 2018. Spatial Organization and Molecular Correlation of TumorInfiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell reports, 23:1, 181.

Samala, R. K., Chan, H.-P., Hadjiiski, L. M., Cha, K. and Helvie, M. A. (2016). Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis. Medical Imaging 2016: Computer-Aided Diagnosis, International Society for Optics and Photonics,
97850Y.

Sarıtaş, M. Z. 2015. Adli tıp uygulamalarında 3D (üç boyutlu) teknolojinin kullanımı.

Schirrmeister, R., Gemein, L., Eggensperger, K., Hutter, F. and Ball, T. (2017). Deep Learning with Convolutional Neural Networks for Decoding and Visualization of EEG Pathology. Signal Processing in Medicine and Biology Symposium (SPMB), 2017 IEEE, IEEE, 1-7.

Sengur, A. (2016). Derin Aşırı Öğrenme Makinesi ile Yüz Tanıma. International Conference on Artificial Intelligence and Data Processing (IDAP), Fırat Üniversitesi, Elazığ, Türkiye.

Seçkin, S. (2014). Türkiye’de Patolojinin Kısa Tarihçesi. https://patoloji.gen.tr/turkiyede-patolojinin-kisatarihcesi/.(Erişim tarihi: 10.01.2019).

Sezgin, N. 2016. Epileptik EG İşaretlerin Aşırı Öğrenme Makineleri ile Sınıflandırılması. DÜMF Mühendislik Dergisi, 7:3, 481-490.

Sinecen, M., Cinar, M., Karal, O., Engin, M., Atesci, Y. Z.. Makinaci, M. ve Cakmak, B. (2009). Diagnosis of Prostat Cancer using Artificial Neural Networks. 2009 14th National Biomedical Engineering Meeting.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15:1, 1929-1958.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, 1-9.

Şeker, A., Diri, B. ve Balık, H. H. 2017. Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3:3, 47-64.

Tanyıldızı, E. ve Okur, S. 2016. Retina Görüntülerindeki Kan Damarlarının Belirlenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 28:2.

Trebeschi, S., van Griethuysen, J. J., Lambregts, D. M., Lahaye, M. J., Parmer, C., Bakers, F. C., Peters, N. H., Beets-Tan, R. G. and Aerts, H. J. 2017. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Scientific reports, 7:1, 5301.

Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E. and Sitti, M. 2018. Deep endovo: A recurrent convolutional neural network (rcnn) based visual odometry approach for endoscopic capsule robots. Neurocomputing, 275, 1861-1870.

Vargas, R., Mosavi, A. and Ruiz, L. 2017. Deep Learning: A Review. Advances in Intelligent Systems and Computing, 5:2.

Veta, M., Pluim, J. P., Van Diest, P. J. and Viergever, M. A. 2014. Breast cancer histopathology image analysis: A review. IEEE Transactions on Biomedical Engineering, 61:5, 1400-1411.

Vieira, S., Pinaya, W. H. and Mechelli, A. 2017. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58-75.

Widrow, B. and Hoff, M. E. (1960). Adaptive switching circuits. Retrieved from Xiao, K., Wang, Z., Xu, T. and Wan, T. 2017. A Deep Learnıng Method For Detectıng And Classıfyıng Breast Cancer Metastases In Lymph Nodes On Hıstopathologıcal Images.

Xie, D., Zhang, L. and Bai, L. 2017. Deep learning in visual computing and signal processing. Applied Computational Intelligence and Soft Computing, 2017.

Xu, J., Janowczyk, A., Chandran, S. and Madabhushi, A. (2010). A Weighted Mean Shift, Normalized Cuts Initialized Color Gradient Based Geodesic Active Contour Model: Applications to Histopathology Image Segmentation. Medical Imaging 2010:Image Processing, International Society for Optics and Photonics, 1-11.

Xu, J., Janowczyk, A., Chandran, S. and Madabhushi, A. 2011. A High-Throughput Active Contour Scheme for Segmentation of Histopathological Imagery. Medical image analysis, 15:6, 851- 862.

Xu, Y., Jia, Z., Wang, L.-B., Ai, Y., Zhang, F., Lai, M., Eric, I. and Chang, C. 2017. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC bioinformatics, 18:1, 281.

Yalçin, N., Alver, S. and Uluhatun, N. (2018). Classification of Retinal Images with Deep Learning for Early Detection of Diabetic Retinopathy Disease. 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, 1-4.

Yonekura, A., Kawanaka, H., Prasath, V. S., Aronow, B. J. and Takase, H. (2017). Glioblastoma Multiforme Tissue Histopathology Images Based Disease Stage Classification with Deep CNN. Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT), 2017 6th International Conference on, IEEE, 1-5.

Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y. and Fan, Y. 2018. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical image analysis, 43, 98-111

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