Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM-Reference-Cited by-同舟云学术

Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM

Published:2016-04-06 Issue:2 Volume:87 Page:179-196
ISSN:1939-8018
Container-title:Journal of Signal Processing Systems
language:en
Short-container-title:J Sign Process Syst

Author:

de Carvalho Filho Antonio Oseas,Silva Aristófanes Corrêa,de Paiva Anselmo Cardoso,Nunes Rodolfo Acatauassú,Gattass Marcelo

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Modelling and Simulation,Information Systems,Signal Processing,Theoretical Computer Science,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11265-016-1134-5.pdf

Reference34 articles.

1. Akram, S., Javed, M.Y., Hussain, A., Riaz, F., & Akram, M.U. (2015). Intensity-based statistical features for classification of lungs ct scan nodules using artificial intelligence techniques. Journal of Experimental & Theoretical Artificial Intelligence, 27(6), 737–751. doi: 10.1080/0952813X.2015.1020526 .

2. Al-Absi, H., Samir, B., Shaban, K., & Sulaiman, S. (2012). Computer aided diagnosis system based on machine learning techniques for lung cancer. In 2012 International conference on computer information science (ICCIS) (Vol. 1, pp. 295–300). doi: 10.1109/ICCISci.2012.6297257 .

3. Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., Kazerooni, E.A., MacMahon, H., Van Beeke, E.J.R., Yankelevitz, D., Biancardi, A.M., Bland, P.H., Brown, M.S., Engelmann, R.M., Laderach, G.E., Max, D., Pais, R.C., Qing, D.P.Y., Roberts, R.Y., Smith, A.R., Starkey, A., Batrah, P., Caligiuri, P., Farooqi, A., Gladish, G.W., Jude, C.M., Munden, R.F., Petkovska, I., Quint, L.E., Schwartz, L.H., Sundaram, B., Dodd, L.E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A.V., Gupte, S., Sallamm, M., Heath, M.D., Kuhn, M.H., Dharaiya, E., Burns, R., Fryd, D.S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., & Croft, B.Y. (2011). The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Medical Physiology, 38(2), 915–931. http://www.biomedsearch.com/nih/Lung-Image-Database-Consortium-LIDC/21452728.html .

4. Baxevanis, A.D., & Ouellette, B.F.F. (2004). Bioinformatics: a practical guide to the analysis of genes and proteins. Methods of biochemical analysis. Wiley. http://books.google.com.br/books?id=ghUZaEAdHUC .

5. Bolboaca, S.D., Jantschi, L., Sestraa, A.F., Sestra, R.E., & Pamfil, D.C. (2011). Pearson-fisher chi-square statistic revisited. Information, 2(3), 528–545. doi: 10.3390/info2030528 . http://www.mdpi.com/2078-2489/2/3/528 .

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hybrid active shape model and deep neural network approach for lung cancer detection;International Journal of Information Technology;2024-04-28

2. A Diagnosis Model Based on Federated Learning for Lung Cancer Classification;Communications in Computer and Information Science;2024

3. Development of a Convolutional Neural Network for Detection of Lung Cancer Based on Computed Tomography Images;2023 Fourth International Conference on Information Systems and Software Technologies (ICI2ST);2023-11-22

4. R-LSTM-CNN Framework Based Lung Cancer Detection and Classification from Chest CT Images;2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS);2023-10-18

5. Skip and chain connected deep fusion network for lung cancer screening;Multimedia Tools and Applications;2023-10-03