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Articles

Recognition of the Gastric Molecular Image Based on Decision Tree and Discriminant Analysis Classifiers by using Discrete Fourier Transform and Features

References

  • Aki, M. O. 2017. Sürücü uykululuğunun gerçek Zamanlı Görüntü İşleme ve Makine Öğrenmesi Teknikleri ile Tespitine Yönelik Bir Sistem Tasarımı ve Uygulaması. Turkey: Trakya Üniversitesi, Fen Bilimleri Enstitüsü.
  • Alpaydin, E. 2014. Introduction to machine learning. Microtome Publishing, MA:MIT press.
  • Bengio, Y., et al. 2004. Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. Advances in neural information processing systems, Montréal, Kanada, 177–84.
  • Cosatto, E., Laquerre, P. F., Malon, C., Graf, H. P., Saito, A., Kiyuna, T., ... & Kamijo, K. I. 2013. Automated gastric cancer diagnosis on h&e-stained sections; ltraining a classifier on a large scale with multiple instance machine learning. In Medical Imaging 2013: Digital Pathology (Vol. 8676, p. 867605). International Society for Optics and Photonics.
  • Das, R., I. Turkoglu, and A. Sengur. 2009. Diagnosis of valvular heart disease through neural networks ensembles. Computer Methods and Programs in Biomedicine 93 (2):185–91. doi:10.1016/j.cmpb.2008.09.005.
  • De Souza, L. A., L. C. S. Afonso, C. Palm, and J. P. Papa 2017. Barrett’s esophagus identification using optimum-path forest. Graphics, Patterns and Images (SIBGRAPI), 2017 30th SIBGRAPI Conference on October, Niteroi, Brazil, 308–14. IEEE
  • Ergün, U. 2009. The classification of obesity disease in logistic regression and neural network methods. Journal of Medical Systems 33 (1):67–72. doi:10.1007/s10916-008-9165-5.
  • Ergün, U., S. Serhatlioğlu, F. Hardalaç, and I. Güler. 2004. Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression. Computers in Biology and Medicine 34 (5):389–405. doi:10.1016/S0010-4825(03)00085-4.
  • Güney, M., and N. Arıca. 2009. Desen Tabanlı İlgi Bölgesi Tespiti. Journal of Naval Science and Engineering 5 (1):94–106.
  • Hardalaç, F., A. T. Ozan, N. Barişçi, U. Ergün, S. Serhatlioğlu, and I. Güler. 2004. The examination of the effects of obesity on a number of arteries and body mass index by using expert systems. Journal of Medical Systems 28 (2):129–42.
  • Işık, Ş. 2014. A comparative evaluation of well-known feature detectors and descriptors. International Journal of Applied Mathematics, Electronics and Computers 3 (1):1–6. doi:10.18100/ijamec.60004.
  • Karakitsos, P., T. M. Megalopoulou, A. Pouliakis, M. Tzivras, A. Archimandritis, and A. Kyroudes. 2004. Application of discriminant analysis and quantitative cytologic examination to gastric lesions. Analytical and Quantitative Cytology and Histology/The International Academy of Cytology [And] American Society of Cytology 26 (6):314–22.
  • Korkmaz, S. A., & Binol, H. (2018). Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early gastric cancer detection. Journal of Molecular Structure, 1156, 255-263.
  • Korkmaz, S. A, et al. 2017. A expert system for gastric cancer images with artificial neural network by using HOG features and linear discriminant analysis: HOG_LDA_ANN. Intelligent Systems and Informatics (SISY), 2017 IEEE 15th International Symposium on. IEEE, 000339–42
  • Kourou, K., T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis. 2015. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal 13:8–17. doi:10.1016/j.csbj.2014.11.005.
  • Leutenegger, S., M. Chli, and R. Y. Siegwart. 2011. BRISK: Binary robust invariant scalable keypoints,” Computer Vision (ICCV), 2011 IEEE International Conference on, Barcelona, Spain. 2548–55
  • Matas, J., O. Chum, M. Urban, and T. Pajdla 2002. Robust wide-baseline stereo from maximally stable extremal regions, British Machine Vision Conference, S.384–393
  • Matas, J., O. Chum, M. Urban, and T. Pajdla. 2004. Robust wide-baseline stereo from maximally stable extremal regions. Image And Vision Computing 22 (10):761–67. doi:10.1016/j.imavis.2004.02.006.
  • Ozcift, A., and A. Gulten. 2011. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer Methods and Programs in Biomedicine 104 (3):443–51. doi:10.1016/j.cmpb.2011.03.018.
  • Özkan, Y. 2008. Veri madenciliği yöntemleri. Turkey: Papatya Yayıncılık Eğitim.
  • Şengür, A. 2008. An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnostic of valvular heart diseases. Computers in Biology and Medicine 38:329–38. doi:10.1016/j.compbiomed.2007.11.004.
  • Sengur, A. 2012. Support vector machine ensembles for intelligent diagnosis of valvular heart disease. Journal of Medical Systems 36 (4):2649–55. doi:10.1007/s10916-011-9740-z.
  • Shayan, Z., N. Mohammad Gholi Mezerji, L. Shayan, and P. Naseri. 2016. Prediction of depression in cancer patients with different classification criteria, linear discriminant analysis versus logistic regression. Global Journal of Health Science 8 (7):41. doi:10.5539/gjhs.v8n7p41.
  • Van Der Maaten, L., E. Postma, and J. Van Den Herik. 2009. Dimensionality reduction: A comparative. Journal of Machine Learning Research 10:66–71.
  • Vasilakakis, M., et al. 2016. Weakly-supervised lesion detection in video capsule endoscopy based on a bag-of-colour features model. International Workshop on Computer-Assisted and Robotic Endoscopy. Athens, Greece: Springer, Cham.
  • Yildirim, H., H. B. Altýnsoy, N. Barýpçý, U. Ergün, E. Oğur, L. F. Hardalaç, and I. Güler. 2004. Classification of the frequency of carotid artery stenosis with MLP and RBF neural networks in patients with coroner artery disease. Journal of Medical Systems 28 (6):591–601.
  • Yoshihiro, S., H. Ryukichi, Y. Tetsuro, H. Norihiro, M. Tatsuya, et al. 2010. Computer-aided estimation for the risk of development of gastric cancer by image processing. Artificial Intelligence in Theory and Practice III 331:197–204. 15.

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