Details

PDF BIBTEX RIS

Title

Multi-feature ensemble system in the renal tumour classification task

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

3

Affiliation

Osowska-Kurczab, Aleksandra Maria : Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland ; Markiewicz, Tomasz : Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland ; Markiewicz, Tomasz : Military Institute of Medicine, ul. Szaserów 128, 04-141 Warsaw, Poland ; Dziekiewicz, Miroslaw : Military Institute of Medicine, ul. Szaserów 128, 04-141 Warsaw, Poland ; Lorent, Malgorzata : Military Institute of Medicine, ul. Szaserów 128, 04-141 Warsaw, Poland

Authors

Osowska-Kurczab, Aleksandra Maria ; Markiewicz, Tomasz ; Dziekiewicz, Miroslaw ; Lorent, Malgorzata

Keywords

medical imaging ; renal cell carcinoma ; convolutional neural networks ; textural features ; support vector machine ; computer vision ; deep learning

Divisions of PAS

Nauki Techniczne

Coverage

e136749

Bibliography

  1.  “Kidney cancer statistics”, Cancer Research UK, (2020). [Online]. Available: https://www.cancerresearchuk.org/health-professional/ cancer-statistics/statistics-by-cancer-type/kidney-cancer. [Accessed: 05-Oct-2020].
  2.  L. Zhou et al., “A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors”, Translational Oncology 12(2), 292‒300, (2019).
  3.  H. Coy et al., “Deep learning and radiomics: the utility of Google TensorFlowTM Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT”, Abdominal Radiology 44(6), 2009‒2020, (2019).
  4.  S. Tabibu, P.K. Vinod, and C.V. Jawahar, “Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning”, Scientific Reports 9(1), 10509, (2019).
  5.  S. Han, S.I. Hwang, and H.J. Lee, “The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method”, Journal of Digital Imaging 32, 638–643, (2019).
  6.  Q. Chaudry, S.H. Raza, A.N. Young, and M.D.Wang, “Automated renal cell carcinoma subtype classification using morphological, textural and wavelets based features”, Journal of Signal Processing Systems 55(1‒3), 15‒23, (2009).
  7.  B. Kocak et al., “Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation”, European Journal of Radiology, 107, 149‒157, (2018).
  8.  S.P. Raman, Y. Chen, J.L. Schroeder, P. Huang, and E.K. Fishman, “CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology”, Academic Radiology, 12, 1587‒1596, (2014).
  9.  W. Sun, B. Zheng, and W. Qian, “Computer aided lung cancer diagnosis with deep learning algorithms”, Proceedings of the International Society for Optics and Photonics Conference (2016).
  10.  H. Polat and D.M. Homay, “Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture”, Applied Sciences, 9(5), 940, (2019).
  11.  W. Alakwaa, M. Nassef, and A. Badr, “Lung cancer Detection and Classification with 3D Convolutional Neural Network (3DCNN)”, International Journal of Advanced Computer Science and Applications (IJACSA) 8(8), (2017).
  12.  M.A. Hussain, G. Hamarneh, and R. Garbi, “Renal Cell Carcinoma Staging with Learnable Image Histogram-Based Deep Neural Network”, Lecture Notes in Computer Science, 11861, 533‒540, (2019).
  13.  O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, Medical Image Computing and Computer-Assisted Intervention (MICCAI), 9351, (2015).
  14.  K. Yin et al., “Deep learning segmentation of kidneys with renal cell carcinoma”, Journal of Clinical Oncology 37(15), (2019).
  15.  J. Kurek et al., “Deep learning versus classical neural approach to mammogram recognition”, Bul. Pol. Acad. Sci. Tech. Sci. 66(6), 831‒840, (2018).
  16.  A. Osowska-Kurczab, T. Markiewicz, M. Dziekiewicz and M. Lorent, “Textural and deep learning methods in recognition of renal cancer types based on CT images”, Proceedings of the International Joint Conference on Neural Networks (IJCNN), (2020).
  17.  A. Osowska-Kurczab, T. Markiewicz, M. Dziekiewicz, and M. Lorent, “Combining texture analysis and deep learning in renal tumour classification task”, Proceedings of the Computational Problems of Electrical Engineering (CPEE), (2020).
  18.  R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification”, IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), (1973).
  19.  A.F. Costa, G. Humpire-Mamani, and A.J.M. Traina, “An Efficient Algorithm for Fractal Analysis of Textures,”, Proceedings of 25th SIBGRAPI Conference on Graphics, Patterns and Images, 39‒46, (2012).
  20.  P. Shanmugavadivu and V. Sivakumar, “Fractal Dimension Based Texture Analysis of Digital Images”, Procedia Engineering, 38, 2981‒2986, (2012).
  21.  M. Unser, “Local Linear Transforms for Texture Analysis”, Proceedings of the 7th IEEE International Conference on Pattern Recognition (ICPR), II, 1206‒1208, (1984).
  22.  M. Unser, “Sum and difference histograms for texture classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 118‒125, (1986).
  23.  C. Cortes and V. Vapnik, “Support-vector network”, Machine Learning 20(3), 273‒297, (1995).
  24.  Y. Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning 2 (1), 1–127, (2009).
  25.  Y. Bengio, Y. LeCun, and G. Hinton, “Deep Learning”, Nature 521, 436–444, (2015).
  26.  I. Goodfellow, Y. Bengio, and A. Courville: Deep Learning, MIT Press, 2016.
  27.  A. Krizhevsky, I. Sutskever, and G. Hinton, “Image net classification with deep convolutional neural networks”, Advances in Neural Information Processing Systems 25, 1‒9, (2012).
  28.  K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 770‒778, (2016).
  29.  C. Szegedy et al., “Going deeper with convolutions”, Proceedings of the 28th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1‒9, (2015).
  30.  C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision”, Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2818‒2826, (2016).
  31.  C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inceptionv4, Inception-ResNet and the impact of residual connections on learning”, Proceedings of 31st Association for the Advancement of Artificial Intelligence on Artificial Intelligence (AAAI), 1‒12, (2016).
  32.  P.N. Tan, M. Steinbach, and V. Kumar: Introduction to Data Mining, Pearson Education, Boston, 2006.
  33.  H. Moch, A.L. Cubilla, P.A. Humphrey, V.E. Reuter, and T.M. Ulbright, “The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours”, European Urology, 70(1), 93‒105, (2016).
  34.  T. Gudbjartsson et al., “Renal oncocytoma: a clinicopathological analysis of 45 consecutive cases”, BJU International 96(9), 1275‒1279, (2005).

Date

10.03.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.136749

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e136749
×