Patoloji Görüntülerinin Derin Öğrenme Yöntemleri İle Sınıflandırılması

Yıl 2022, Sayı: 33, 192 - 206, 31.01.2022

Meral Karakurt , İsmail İşeri

https://doi.org/10.31590/ejosat.1011091

Cited By: 3

Öz

Meme kanseri, kadınlar arasında en çok görülen kanser türüdür. Kanserin erken tespit edilmesi, tedavinin zamanında yapılmasını sağlamaktadır. 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 yöntemi kullanılmaktadır. Bu yöntemlerle görüntülerden anlamlı bilgiler çıkarılarak anormalliklerin tespit edilmesi veya tahmin edilmesi işlemleri yapılmaktadır. Yapay zeka yöntemlerinden biri olan derin öğrenme, patoloji 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, patoloji görüntülerinin analizi için en çok tercih edilen yöntemlerden biri haline gelmiştir.
Bu çalışmada, derin öğrenme mimarilerinden evrişimsel sinir ağı (Convolutional Neural Networks, CNN) kullanılarak, meme kanserine ilişkin patolojik görüntülerin sınıflandırması işlemi gerçekleştirilmiştir. Farklı sayıda filtre ve katman kullanılarak tasarlanan modellerin eğitimi ve test işlemleri için 60000 adet meme patoloji görüntüsünden oluşan bir veri seti kullanılmıştır. Model eğitimleri için donanım olarak Google Colab üzerinde NVIDIA Tesla K80 GPU işlemcili bir makine kullanılmış olup yazılım aracı olarak açık kaynak kodlu Keras kütüphanesi ve Python programlama dili kullanılmıştır. 3 adet evrişim katmanı, 3 adet ReLU katmanı, 3 adet havuzlama katmanı ve tam bağlantılı katmanda 200 sinir hücresi kullanılarak eğitilen model ile kanserli ve kanserli olmayan patoloji görüntülerinin sınıflandırılmasında doğruluk değeri 0.8775, F1 skoru 0.8238, hassasiyet değeri 0.8381, hatırlama değeri 0.8762, MSE değeri 0.1195, MAE değeri 0.2497 elde edilmiştir. Elde edilen yüksek doğruluk, F1 skoru, hassasiyet ve hatırlama değerleri ile düşük hata değerleri, bu tez kapsamında önerilen CNN modelinin patoloji görüntülerinin sınıflandırılmasında kullanılabileceğini; özellikle tıp fakültelerinin ve hastanelerin patoloji bölümlerinde kullanılabilir sistemler tasarlanabileceğini göstermektedir.

Anahtar Kelimeler

Patoloji Görüntüleri, Özellik Çıkarımı, Derin Öğrenme, Evrişimsel Sinir Ağları, Sınıflandırma.

Classification Of Pathology Images Using Deep Learning Methods

Öz

Breast cancer is the most common type of cancer among women. Early detection of cancer ensures timely treatment. Medical images are of vital importance in the detection of diseases existing in the bodies of living things or in the early diagnosis of diseases that may occur. Pathology images, which are especially used for cancer detection, are a type of medical image that is transferred to the computer environment by dipping a part of a patient into various chemical substances and scanning with special machines. Many machine learning and artificial intelligence methods are used to analyze medical images. With these methods, meaningful information is extracted from the images and anomalies are detected or predicted. Deep learning, one of the artificial intelligence methods, shows significant success in analyzing pathology images. Deep learning architectures perform feature extraction in machine learning studies with convolution layers within the architecture. Deep learning algorithms used for operations such as image classification, object recognition and segmentation have become one of the most preferred methods for the analysis of pathology images.

In this study, classification of pathological images related to breast cancer was carried out by using convolutional neural network (Convolutional Neural Networks, CNN), which is one of the deep learning architectures. A data set consisting of 60000 breast pathology images was used for training and testing of models designed using different numbers of filters and layers. For model trainings, a machine with NVIDIA Tesla K80 GPU processor on Google Colab was used as hardware, and the open source Keras library and Python programming language were used as software tools. The model, which was trained by using 3 convolution layers, 3 ReLU layers, 3 pooling layers and 200 neurons in a fully connected layer, obtained 0.8775 accuracy value, 0.8238 F1 score, 0.8381 precision value, 0.8762 recall value, 0.1195 MSE value and 0.2497 MAE value in the classification of cancerous and non-cancerous pathology images. Obtained high accuracy, F1 score, precision and recall values with low error values, can be used in the classification of pathology images of the proposed CNN model within the scope of this thesis; shows usable systems can be designed especially in the pathology departments of medical faculties and hospitals.

Anahtar Kelimeler

Breast Cancer, Pathology Images, Feature Extraction, Deep Learning, Convolutional Neural Networks, Keras, Classification..

Kaynakça

• Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L. ve 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. ve 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. ve Frey, B. J. 2015. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology, 33:8, 831.
• Anonim 2008. Biyomedikal cihaz teknolojileri Projesi) M M E v Ö S G (Ed.)
• Anonim. 2019. Gradyan İniş Optimizasyon Algoritmalarına Genel Bakış. https://devhunteryz.wordpress.com/2019/06/04/gradyan-inis-optimizasyon-algoritmalarina-genel-bakis/. (Erişim tarihi: 01.07.2019).
• 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. ve 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. ve 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.
• Belsare, A. ve 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. ve Xie, Z. 2018. Deep Learning and Its Applications in Biomedicine. Genomics, proteomics & bioinformatics, 16, 17-32.
• Carrio, A., Sampedro, C., Rodriguez-Ramos, A. ve Campoy, P. 2017. A review of deep learning methods and applications for unmanned aerial vehicles. Journal of Sensors, 2017.
• Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. ve 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. ve Wang, G. 2017a. Low-dose CT via convolutional neural network. Biomedical optics express, 8:2, 679-694.
• Chen, L., Bentley, P. ve Rueckert, D. 2017b. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage: Clinical, 15, 633-643.
• Cireşan, D. C., Giusti, A., Gambardella, L. M. ve 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. ve Meyer, J. S. (2008). Grading Nuclear Pleomorphism on Histological Micrographs. Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, IEEE, 1-4.
• Danaee, P., Ghaeini, R. ve 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. ve 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. ve Bengio, Y. 2014. Brain Tumor Segmentation with Deep Neural Networks. 1-5.
• Deniz, C. M., Xiang, S., Hallyburton, R. S., Welbeck, A., Babb, J. S., Honig, S., Cho, K. ve 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. Ekmekji, A. 2016. Technical Report. Stanford University.
• Fakoor, R., Ladhak, F., Nazi, A. ve 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. ve 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. ve 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. ve 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. ve Miyake, S. 1982. Competition and cooperation in neural nets. Springer, 267-285. Gandomkar, Z., Brennan, P. C. ve Mello-Thoms, C. 2018. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artificial intelligence in medicine.
• Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T. ve 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. ve 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.
• 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. ve 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.
• Hinton, G. E., Osindero, S. ve Teh, Y.-W. 2006. A fast learning algorithm for deep belief nets. Neural computation, 18:7, 1527-1554.
• Hinton, G. E. ve Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks (0036-8075). Retrieved from
• 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. ve 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.
• Ivakhnenko, A. G. e. ve Lapa, V. G. 1965. Cybernetic predicting devices. CCM Information Corporation.
• İlkılıç Aytaç, Z., İşeri, İ. & Dandıl, B. (2021). Derin Öğrenme Kullanarak Tiroid Kanseri Teşhisi. Avrupa Bilim ve Teknoloji Dergisi, (29), 292-298.
• Janowczyk, A. ve Madabhushi, A. 2016. Deep Learning for Digital Pathology Image Analysis: A Comprehensive Tutorial with Selected Use Cases. Journal of pathology informatics, 7.
• 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. ve Hajirasouliha, I. 2018. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine, 27, 317-328.
• Kohl, M., Walz, C., Ludwig, F., Braunewell, S. ve 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. ve Chitalia, V. C. 2018. Association of pathological fibrosis with renal survival using deep neural networks. Kidney international reports, 3:2, 464-475.
• Komura, D. ve 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. ve Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 1097-1105.
• Kvam, J. ve 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. ve Hinton, G. 2015. Deep learning. nature, 521:7553, 436-442.
• LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. ve Jackel, L. D. 1989. Backpropagation applied to handwritten zip code recognition. Neural computation, 1:4, 541-551.
• LeCun, Y., Bottou, L., Bengio, Y. ve Haffner, P. 1998. Gradient-based 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. ve 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. ve 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. ve 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. ve Mun, S. K. 1995. Artificial convolution neural network for medical image pattern recognition. Neural networks, 8:7-8, 1201-1214.
• Madabhushi, A. ve Lee, G. 2016. Image analysis and machine learning in digital pathology: Challenges and opportunities: Elsevier.
• McCulloch, W. S. ve 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. ve 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. ve Hajirasouliha, I. 2018. Breast Cancer Histopathological Image Classification: A Deep Learning Approach. bioRxiv, 242818.
• Nedzved, A., Belotserkovsky, A., Lehmann, T. ve 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. ve Madabhushi, A. 2017. Deep Learning for Medical Image Analysis. Elsevier, 179-195.
• Özçelik, Y. B. & Altan, A. (2021). Diyabetik Retinopati Teşhisi için Fundus Görüntülerinin Derin ÖğrenmeTabanlı Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (29), 156-167.
• 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. ve 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.
• Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S. ve Thoma, G. 2018. Image analysis and machine learning for detecting malaria. Translational Research, 194, 36-55.
• Rani, R. U. ve 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 54-59.
• 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. ve Nasipuri, M. 2019. Patch-Based System for Classification of Breast Histology Images Using Deep Learning. Computerized Medical Imaging and Graphics, 71, 90-103.
• Rumelhart, D. E., Hinton, G. E. ve Williams, R. J. 1986. Learning representations by back-propagating errors. nature, 323:6088, 533-535.
• Sabeena, B. K., Nair, M. S. ve 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. ve Racoceanu, D. 2018. Efficient Deep Learning Model for Mitosis Detection Using Breast Histopathology Images. Computerized Medical Imaging and Graphics, 64, 29-40.
• Samala, R. K., Chan, H.-P., Hadjiiski, L. M., Cha, K. ve 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. ve 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.
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. ve 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. ve 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. ve 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. ve 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. ve Ruiz, L. 2017. Deep Learning: A Review. Advances in Intelligent Systems and Computing, 5:2.
• Veta, M., Pluim, J. P., Van Diest, P. J. ve 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. ve 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. ve Hoff, M. E. (1960). Adaptive switching circuits. Retrieved from
• World Health Organization. (2020). Breast Cancer. 12 Ekim 2021 tarihinde https://www.who.int/news-room/fact-sheets/detail/breast-cancer adresinden erişildi.
• Xiao, K., Wang, Z., Xu, T. ve Wan, T. 2017. A Deep Learnıng Method For Detectıng And Classıfyıng Breast Cancer Metastases In Lymph Nodes On Hıstopathologıcal Images.
• Xie, D., Zhang, L. ve Bai, L. 2017. Deep learning in visual computing and signal processing. Applied Computational Intelligence and Soft Computing, 2017.
• Xu, J., Janowczyk, A., Chandran, S. ve 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. ve 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. ve 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. ve 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. ve 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. ve Fan, Y. 2018. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical image analysis, 43, 98-111.