Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 11 Sayı: 3, 802 - 812, 31.12.2019
https://doi.org/10.29137/umagd.640667

Öz

Kaynakça

  • Ahmed J., Aljaaf and friends. (2018). Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics. IEEE Evrimsel Hesaplama Kongresi (CEC). pp. 1-9.
  • Amelia, J., Averitt and Karthik, N. (2018). Going Deep: The Role of Neural Networks for Renal Survival and Beyond, Kidney IntRep, 3, pp.242–243.
  • American Cancer Society. (2008). Estimated New Cancer Cases and Deaths by Gender in All Regions. Retrieved from www.cancer.org.
  • Anusorn, C., Thipwan F., and friends. (2016). Predictive Analytics for Chronic Kidney Disease Using Machine Learning Techniques, MITICON.
  • Bengio, Y. P., Lamblin, Popovici, D. and Larochelle, H. (2006). Greedy layer-wise training of deep networks. Proceedings of the 19th International Conference on Neural Information Processing Systems. MIT Press, pp. 153–160.
  • Carreira, M. A. and Hinton, G. E. (2005). On Contrastive Divergence Learning. Artif. Intell. Stat., Vol. 10.
  • Chi-Jim, C., Tun-Wen, P. and friends. (2014). Stage Diagnosis for Chronic Kidney Disease Based on Ultrasonography. 11th International Conference on Fuzzy Systems and Knowledge Discovery.
  • Doğruyol, M., Aydın, A. (2017). Topluluk sınıflandırıcılarını kullanarak kronik böbrek hastalığının saptanması. ELECO 2017, pp. 544-547.
  • Elman, J. L. (1991). Finding Structure in Time. Cogn. Sci., vol. 14, No.2, pp. 179–211.
  • Emily, S. Blum and friends. (2018). Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning. The journal of Urology, pp.847-852.
  • Eroğlu, K., Palabaş, T. (2016). Kronik Böbrek Hastalığı Tespitinde Farklı Sınıflandırma Yöntemleri ve Farklı Topluluk Algoritmalarının Birlikte Kullanımının Sınıflandırma Performansına Etkisi., ELECO, pp. 512 – 516.
  • Eskisehir. (2017). Anadolu International Conference in Economics V, May 11-13, Eskisehir, Turkey.
  • Fukushim, K. N. (1980). A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., Vol. 36, No. 4, pp. 193–202.
  • Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science (80-.), Vol. 313, No.5786, pp. 504–507.
  • Hinton, G. E., Osindero, S. and the, Y.W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Comput. Vol. 18, No. 7, pp. 1527–1554.
  • Hubel, D. H. and Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. J. Physiol., Vol. 195, pp. 215–243.
  • Jon N. M. and friends. (2018). Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections, IEEE Transactıons On Medıcal Imagıng.
  • Jon, N. M., Matthew, K., Satoru, K., Ta-Chiang, L., Thaddeus, S., Stappenbeck, Joseph P. G., and Joshua, S., Swamidass. (2018). Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.
  • Koçak, B. and friends. (2018). Renal hucrelik karsinom alt tipleri arasındaki dokusal farklılıklar, Avrupa Radyoloji Dergisi, pp. 149-157.
  • Krizhevsky, A. and Hinton, G. E. (2011). Using Very Deep Autoencoders for Content Based Image Retrieval. In European Symposium on Artificial Neural Networks, pp. 489–494.
  • Lecun, Y., Bottou L., Bengio Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE, Vol. 86, No. 11, pp. 2278–2324.
  • Leyi, W., Yijie, D., Ran, S., Jijun, T., Quan, Z. (2018). Parallel Distrib. Comput.212-217.
  • Liang, Y., Tang, Z., Meng, Y., Liu, J. (2018). Object detection based on deep learning for urinesediment examination, biocybernetics and biomedical engineering. pp.661-670.
  • Lorenzo, A. J., Rickard, M., Luis, H., Braga, Y., Oliveria,, J. (2018). Predictive Analytics and Modeling Employing Machine Learning Technology, Urology.
  • Matt., S., Martin P., John, H., Phanl, and May D. (2015). Integration of Multimodal RNA-Seq Data for Prediction of Kidney Cancer Survival. IEEE International Conference on Bioinformatics and Biomedicine (BTBM).
  • Mehmet Akif Ersoy U. (2017). Artificial Intelligence and Machine Learning Applications in Big Data Analysis. Journal of Social Sciences Institute. Vol. 9, No.22, pp.155-172.
  • Mercaldo, F., Nardone, V., Santone, A. (2017). Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques, International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2017, Marseille, France.
  • Mikolov, T. (2010). Recurrent neural network based language model, in Interspeech.
  • Monika, R. and Uvais, Q. (2003). Yıllık Teknik Konferans IEEE Bölgesi 5, pp. 23–27.
  • Muratbinbay. (2018). Retrieved from https://www.muratbinbay.com.
  • Ning, K., Xiaoxi, L., Chunyan, L., Jie, L., Hongwei, W. (2017). Deep architecture for Heparin dosage prediction during continuous renal replacement therapy. Proceedings of the 36th Chinese Control Conference July 26-28. Dalian, China.
  • Onkoloji. (2018). Retrieved from http://www.onkoloji.gov.tr.
  • Qiang, Z., Gregory, T., Yong F. (2018). Transfer Learning For Diagnosis Of Congenital Abnormalities Of The Kidney And Urinary Tract In Children Based On Ultrasound Imaging Data, ISBI.
  • Ranzato, M., Huang, F. J., Boureau, Y.L. and LeCun, Y. (2007). Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.
  • Roberto, A., Remi, C., Ketan, B., Vincent, A. (2015). Fast Kidney Detection and Segmentation with Learned Kernel Convolution and Model Deformation in 3d Ultrasound Images. Medisys Research Lab, Philips Research, Suresnes, France.
  • Salakhutdinov, R. and Hinton, G. (2009). Deep Boltzmann Machines,”in International Conference on Artificial Intelligence and Statistics”, pp. 3– 11.
  • Seker, A. and Gürkan, A. (2017). Yuksek, Stacked Autoencoder Method for Fabric Defect Detection. Sci. Sci. J., Vol. 38, No. 2.
  • Seker, A. Diri, B., Balik, H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3, pp.47-64.
  • Shehata, M. And friends. (2016). A new non-invasive approach for early classification of renal rejection types using diffusion-weighted mri, icip.
  • Smolensky, P. (1986). Information Processing in Dynamical Systems: Foundations of Harmony Theory.
  • Tadashi, K., Junji, U. (2015). The 3-Dimensional Medical Image Recognition of Right and Left Kidneys by Deep GMDH-type Neural Network. Track3: Bioinformatics, Medical Imaging and Neuroscience, Okinawa, Japan, ICIIBMS.
  • Tibeter. (2018). Retrieved from https://tibeterdogru.com.
  • Uroweb. (2018). Retrieved from https://patients.uroweb.org/en/ben-bir-uroloji-hastasiyim/bobrekkanseri/bobrek-kanseri-tani-ve-siniflamasi/siniflama.
  • Vijaya, B., and friends. (2018). Renal Survival Using Deep Neural Networks, Kidney IntRep 3, pp.464–475.
  • Vinoth, R., and Bommannaraja, K. (2017). FPGA Design of Efficient Kidney Image Classification using Algebric Histogram Feature Model and Sparse Deep Neural Network (SDNN) Techniques. Conference on Emerging Devices and Smart Systems (ICEDSS).
  • Zheng, Q., Tasian, G., Fan, Y. (2018). Transfer learnıng for dıagnosıs of congenıtal abnormalıtıes of the kıdney and urınary tract in chıldren based on ultrasound ımagıng data. International Symposium on Biomedical Imaging.

Machine Learning of Kidney Tumors and Diagnosis and Classification by Deep Learning Methods

Yıl 2019, Cilt: 11 Sayı: 3, 802 - 812, 31.12.2019
https://doi.org/10.29137/umagd.640667

Öz

Kidney cancer is
difficult to diagnose and it can be quite complicated for physicians to
diagnose. In this study, while providing information about multiple sources to
help people who are dealing with the challenges of the diagnosis of kidney
cancer, in order to serve as a guide the principles of kidney cancer are tried
to be explained. In recent years, many new methods of treatment have been
developed for kidney cancer, and some are under development by scientists.
These studies provide treatment information that offers new hope to the lives
of kidney cancer patients. In this study, it is aimed to get acquainted with
kidney cancer cells by using machine learning, and deep learning algorithms. In
this way, an application can be developed to guide patients and physicians
through early diagnosis and classification.

Kaynakça

  • Ahmed J., Aljaaf and friends. (2018). Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics. IEEE Evrimsel Hesaplama Kongresi (CEC). pp. 1-9.
  • Amelia, J., Averitt and Karthik, N. (2018). Going Deep: The Role of Neural Networks for Renal Survival and Beyond, Kidney IntRep, 3, pp.242–243.
  • American Cancer Society. (2008). Estimated New Cancer Cases and Deaths by Gender in All Regions. Retrieved from www.cancer.org.
  • Anusorn, C., Thipwan F., and friends. (2016). Predictive Analytics for Chronic Kidney Disease Using Machine Learning Techniques, MITICON.
  • Bengio, Y. P., Lamblin, Popovici, D. and Larochelle, H. (2006). Greedy layer-wise training of deep networks. Proceedings of the 19th International Conference on Neural Information Processing Systems. MIT Press, pp. 153–160.
  • Carreira, M. A. and Hinton, G. E. (2005). On Contrastive Divergence Learning. Artif. Intell. Stat., Vol. 10.
  • Chi-Jim, C., Tun-Wen, P. and friends. (2014). Stage Diagnosis for Chronic Kidney Disease Based on Ultrasonography. 11th International Conference on Fuzzy Systems and Knowledge Discovery.
  • Doğruyol, M., Aydın, A. (2017). Topluluk sınıflandırıcılarını kullanarak kronik böbrek hastalığının saptanması. ELECO 2017, pp. 544-547.
  • Elman, J. L. (1991). Finding Structure in Time. Cogn. Sci., vol. 14, No.2, pp. 179–211.
  • Emily, S. Blum and friends. (2018). Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning. The journal of Urology, pp.847-852.
  • Eroğlu, K., Palabaş, T. (2016). Kronik Böbrek Hastalığı Tespitinde Farklı Sınıflandırma Yöntemleri ve Farklı Topluluk Algoritmalarının Birlikte Kullanımının Sınıflandırma Performansına Etkisi., ELECO, pp. 512 – 516.
  • Eskisehir. (2017). Anadolu International Conference in Economics V, May 11-13, Eskisehir, Turkey.
  • Fukushim, K. N. (1980). A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., Vol. 36, No. 4, pp. 193–202.
  • Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science (80-.), Vol. 313, No.5786, pp. 504–507.
  • Hinton, G. E., Osindero, S. and the, Y.W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Comput. Vol. 18, No. 7, pp. 1527–1554.
  • Hubel, D. H. and Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. J. Physiol., Vol. 195, pp. 215–243.
  • Jon N. M. and friends. (2018). Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections, IEEE Transactıons On Medıcal Imagıng.
  • Jon, N. M., Matthew, K., Satoru, K., Ta-Chiang, L., Thaddeus, S., Stappenbeck, Joseph P. G., and Joshua, S., Swamidass. (2018). Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.
  • Koçak, B. and friends. (2018). Renal hucrelik karsinom alt tipleri arasındaki dokusal farklılıklar, Avrupa Radyoloji Dergisi, pp. 149-157.
  • Krizhevsky, A. and Hinton, G. E. (2011). Using Very Deep Autoencoders for Content Based Image Retrieval. In European Symposium on Artificial Neural Networks, pp. 489–494.
  • Lecun, Y., Bottou L., Bengio Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE, Vol. 86, No. 11, pp. 2278–2324.
  • Leyi, W., Yijie, D., Ran, S., Jijun, T., Quan, Z. (2018). Parallel Distrib. Comput.212-217.
  • Liang, Y., Tang, Z., Meng, Y., Liu, J. (2018). Object detection based on deep learning for urinesediment examination, biocybernetics and biomedical engineering. pp.661-670.
  • Lorenzo, A. J., Rickard, M., Luis, H., Braga, Y., Oliveria,, J. (2018). Predictive Analytics and Modeling Employing Machine Learning Technology, Urology.
  • Matt., S., Martin P., John, H., Phanl, and May D. (2015). Integration of Multimodal RNA-Seq Data for Prediction of Kidney Cancer Survival. IEEE International Conference on Bioinformatics and Biomedicine (BTBM).
  • Mehmet Akif Ersoy U. (2017). Artificial Intelligence and Machine Learning Applications in Big Data Analysis. Journal of Social Sciences Institute. Vol. 9, No.22, pp.155-172.
  • Mercaldo, F., Nardone, V., Santone, A. (2017). Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques, International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2017, Marseille, France.
  • Mikolov, T. (2010). Recurrent neural network based language model, in Interspeech.
  • Monika, R. and Uvais, Q. (2003). Yıllık Teknik Konferans IEEE Bölgesi 5, pp. 23–27.
  • Muratbinbay. (2018). Retrieved from https://www.muratbinbay.com.
  • Ning, K., Xiaoxi, L., Chunyan, L., Jie, L., Hongwei, W. (2017). Deep architecture for Heparin dosage prediction during continuous renal replacement therapy. Proceedings of the 36th Chinese Control Conference July 26-28. Dalian, China.
  • Onkoloji. (2018). Retrieved from http://www.onkoloji.gov.tr.
  • Qiang, Z., Gregory, T., Yong F. (2018). Transfer Learning For Diagnosis Of Congenital Abnormalities Of The Kidney And Urinary Tract In Children Based On Ultrasound Imaging Data, ISBI.
  • Ranzato, M., Huang, F. J., Boureau, Y.L. and LeCun, Y. (2007). Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.
  • Roberto, A., Remi, C., Ketan, B., Vincent, A. (2015). Fast Kidney Detection and Segmentation with Learned Kernel Convolution and Model Deformation in 3d Ultrasound Images. Medisys Research Lab, Philips Research, Suresnes, France.
  • Salakhutdinov, R. and Hinton, G. (2009). Deep Boltzmann Machines,”in International Conference on Artificial Intelligence and Statistics”, pp. 3– 11.
  • Seker, A. and Gürkan, A. (2017). Yuksek, Stacked Autoencoder Method for Fabric Defect Detection. Sci. Sci. J., Vol. 38, No. 2.
  • Seker, A. Diri, B., Balik, H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3, pp.47-64.
  • Shehata, M. And friends. (2016). A new non-invasive approach for early classification of renal rejection types using diffusion-weighted mri, icip.
  • Smolensky, P. (1986). Information Processing in Dynamical Systems: Foundations of Harmony Theory.
  • Tadashi, K., Junji, U. (2015). The 3-Dimensional Medical Image Recognition of Right and Left Kidneys by Deep GMDH-type Neural Network. Track3: Bioinformatics, Medical Imaging and Neuroscience, Okinawa, Japan, ICIIBMS.
  • Tibeter. (2018). Retrieved from https://tibeterdogru.com.
  • Uroweb. (2018). Retrieved from https://patients.uroweb.org/en/ben-bir-uroloji-hastasiyim/bobrekkanseri/bobrek-kanseri-tani-ve-siniflamasi/siniflama.
  • Vijaya, B., and friends. (2018). Renal Survival Using Deep Neural Networks, Kidney IntRep 3, pp.464–475.
  • Vinoth, R., and Bommannaraja, K. (2017). FPGA Design of Efficient Kidney Image Classification using Algebric Histogram Feature Model and Sparse Deep Neural Network (SDNN) Techniques. Conference on Emerging Devices and Smart Systems (ICEDSS).
  • Zheng, Q., Tasian, G., Fan, Y. (2018). Transfer learnıng for dıagnosıs of congenıtal abnormalıtıes of the kıdney and urınary tract in chıldren based on ultrasound ımagıng data. International Symposium on Biomedical Imaging.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Fuat Türk 0000-0001-8159-360X

Murat Lüy

Necaattin Barışçı

Yayımlanma Tarihi 31 Aralık 2019
Gönderilme Tarihi 31 Ekim 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 11 Sayı: 3

Kaynak Göster

APA Türk, F., Lüy, M., & Barışçı, N. (2019). Machine Learning of Kidney Tumors and Diagnosis and Classification by Deep Learning Methods. International Journal of Engineering Research and Development, 11(3), 802-812. https://doi.org/10.29137/umagd.640667
Tüm hakları saklıdır. Kırıkkale Üniversitesi, Mühendislik Fakültesi.