INFORMAZIONI SU QUESTO ARTICOLO

Cita

Abdel-Nasser, M., Melendez, J., Moreno, A., Omer, O.A. and Puig, D. (2017). Breast tumor classification in ultrasound images using texture analysis and super-resolution methods, Engineering Applications of Artificial Intelligence59: 84–92.10.1016/j.engappai.2016.12.019Search in Google Scholar

Acharya, U.R., Ng, W.L., Rahmat, K., Sudarshan, V.K., Koh, J.E., Tan, J.H., Hagiwara, Y., Yeong, C.H. and Ng, K.H. (2017). Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm, Biomedical Signal Processing and Control33: 400–410.10.1016/j.bspc.2016.11.004Search in Google Scholar

Cai, L.,Wang, X.,Wang, Y., Guo, Y., Yu, J. andWang, Y. (2015). Robust phase-based texture descriptor for classification of breast ultrasound images, Biomedical Engineering Online14(1): 26.10.1186/s12938-015-0022-8437650025889570Search in Google Scholar

Chang, J.M., Moon, W.K., Cho, N., Yi, A., Koo, H.R., Han, W., Noh, D.-Y., Moon, H.-G. and Kim, S.J. (2011). Clinical application of shear wave elastography (SWE) in the diagnosis of benign and malignant breast diseases, Breast Cancer Research and Treatment129(1): 89–97.10.1007/s10549-011-1627-721681447Search in Google Scholar

Chen, S.-C., Cheung, Y.-C., Su, C.-H., Chen, M.-F., Hwang, T.-L. and Hsueh, S. (2004). Analysis of sonographic features for the differentiation of benign and malignant breast tumors of different sizes, Ultrasound in Obstetrics and Gynecology23(2): 188–193.10.1002/uog.93014770402Search in Google Scholar

Cheng, H.-D., Shan, J., Ju, W., Guo, Y. and Zhang, L. (2010). Automated breast cancer detection and classification using ultrasound images: A survey, Pattern Recognition43(1): 299–317.10.1016/j.patcog.2009.05.012Search in Google Scholar

Daoud, M.I., Bdair, T.M., Al-Najar, M. and Alazrai, R. (2016). A fusion-based approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses, Computational and Mathematical Methods in Medicine2016: 6740956.10.1155/2016/6740956522730728127383Search in Google Scholar

Fawcett, T. (2004). ROC graphs: Notes and practical considerations for researchers, Machine Learning31(1): 1–38.Search in Google Scholar

Fawcett, T. (2006). An introduction to ROC analysis, Pattern Recognition Letters27(8): 861–874.10.1016/j.patrec.2005.10.010Search in Google Scholar

Fischer, L., Hammer, B. and Wersing, H. (2015). Efficient rejection strategies for prototype-based classification, Neurocomputing169: 334–342.10.1016/j.neucom.2014.10.092Search in Google Scholar

Fu, J., Li, Y., Li, N. and Li, Z. (2018). Comprehensive analysis of clinical utility of three-dimensional ultrasound for benign and malignant breast masses, Cancer Management and Research10: 3295–3303.10.2147/CMAR.S176494613223030233245Search in Google Scholar

Gai, S., Zhang, B., Yang, C. and Yu, L. (2018). Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution, Digital Signal Processing72: 192–207.10.1016/j.dsp.2017.10.006Search in Google Scholar

Garcia-Closas, M. et al. (2008). Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics, PLoS Genetics4(4): e1000054.Search in Google Scholar

Guan, H., Zhang, Y., Cheng, H., Xian, M. and Tang, X. (2019). Ba2cs: Bounded abstaining with two constraints of reject rates in binary classification, Neurocomputing357: 125–134.10.1016/j.neucom.2019.04.047Search in Google Scholar

Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research3(Mar): 1157–1182.Search in Google Scholar

Haralick, R.M., Shanmugam, K. and Dinstein, I. (1973). Textural features for image classification, IEEE Transactions on Systems, Man, and CyberneticsSMC- 3(6): 610–621.10.1109/TSMC.1973.4309314Search in Google Scholar

Hofmann, T. (1999). Probabilistic latent semantic analysis, Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, pp. 289–296.Search in Google Scholar

Hong, X., Chen, S. and Harris, C.J. (2007). A kernel-based two-class classifier for imbalanced data sets, IEEE Transactions on Neural Networks18(1): 28–41.10.1109/TNN.2006.882812Search in Google Scholar

Kang, S., Cho, S., Rhee, S.-j. and Yu, K.-S. (2017). Reliable prediction of anti-diabetic drug failure using a reject option, Pattern Analysis and Applications20(3): 883–891.10.1007/s10044-016-0585-4Search in Google Scholar

Lee, J., Nishikawa, R.M., Reiser, I. and Boone, J.M. (2018). Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification, Medical Physics45(8): 3650–3656.10.1002/mp.13054Search in Google Scholar

Li, L., Zhou, X., Zhao, X., Hao, S., Yao, J., Zhong, W. and Zhi, H. (2017). B-mode ultrasound combined with color Doppler and strain elastography in the diagnosis of non-mass breast lesions: A prospective study, Ultrasound in medicine & biology43(11): 2582–2590.10.1016/j.ultrasmedbio.2017.07.014Search in Google Scholar

Liberman, L. and Menell, J.H. (2002). Breast imaging reporting and data system (BI-RADS), Radiologic Clinics of North America40(3): 409–430.10.1016/S0033-8389(01)00017-3Search in Google Scholar

Lin, C.-M., Hou, Y.-L., Chen, T.-Y. and Chen, K.-H. (2014). Breast nodules computer-aided diagnostic system design using fuzzy cerebellar model neural networks, IEEE Transactions on Fuzzy Systems22(3): 693–699.10.1109/TFUZZ.2013.2269149Search in Google Scholar

Liu, Y., Cheng, H., Huang, J., Zhang, Y., Tang, X., Tian, J.-W. andWang, Y. (2012). Computer aided diagnosis system for breast cancer based on color Doppler flow imaging, Journal of Medical Systems36(6): 3975–3982.10.1007/s10916-012-9869-422791011Search in Google Scholar

López, V., Fernández, A., García, S., Palade, V. and Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics, Information Sciences250: 113–141.10.1016/j.ins.2013.07.007Search in Google Scholar

Monticciolo, D.L., Newell, M.S., Hendrick, R.E., Helvie, M.A., Moy, L.,Monsees, B., Kopans, D.B., Eby, P.R. and Sickles, E.A. (2017). Breast cancer screening for average-risk women: Recommendations from the ACR commission on breast imaging, Journal of the American College of Radiology14(9): 1137–1143.10.1016/j.jacr.2017.06.00128648873Search in Google Scholar

Moon, W.K., Chen, I.-L., Yi, A., Bae, M.S., Shin, S.U. and Chang, R.-F. (2018). Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound, Computer Methods and Programs in Biomedicine162: 129–137.10.1016/j.cmpb.2018.05.01129903479Search in Google Scholar

Mousania, Y. and Karimi, S. (2019). Contrast improvement of ultrasound images of focal liver lesions using a new histogram equalization, in K.S. Montaser (Ed.), Fundamental Research in Electrical Engineering, Springer, Singapore, pp. 43–53.10.1007/978-981-10-8672-4_4Search in Google Scholar

Pietraszek, T. (2007). On the use of ROC analysis for the optimization of abstaining classifiers, Machine Learning68(2): 137–169.10.1007/s10994-007-5013-ySearch in Google Scholar

Prati, R.C., Batista, G. and Monard, M.C. (2011). A survey on graphical methods for classification predictive performance evaluation, IEEE Transactions on Knowledge and Data Engineering23(11): 1601–1618.10.1109/TKDE.2011.59Search in Google Scholar

Rahmawaty, M., Nugroho, H. A., Triyani, Y., Ardiyanto, I. and Soesanti, I. (2016). Classification of breast ultrasound images based on texture analysis, International Conference on Biomedical Engineering (IBIOMED), Yogyakarta, Indonesia, pp. 1–6.Search in Google Scholar

Rawashdeh, M., Lewis, S., Zaitoun, M. and Brennan, P. (2018). Breast lesion shape and margin evaluation: BI-RADS based metrics understate radiologists’ actual levels of agreement, Computers in Biology and Medicine96: 294–298.10.1016/j.compbiomed.2018.04.00529673997Search in Google Scholar

Rodriguez-Cristerna, A., Gomez-Flores, W. and de Albuquerque Pereira, W.C. (2018). A computer-aided diagnosis system for breast ultrasound based on weighted bi-rads classes, Computer Methods and Programs in Biomedicine153: 33–40.10.1016/j.cmpb.2017.10.00429157459Search in Google Scholar

Roffo, G., Melzi, S., Castellani, U. and Vinciarelli, A. (2017). Infinite latent feature selection: A probabilistic latent graph-based ranking approach, Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), Venice, Italy, pp. 1407–1415.Search in Google Scholar

Roffo, G., Melzi, S. and Cristani, M. (2015). Infinite feature selection, Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, pp. 4202–4210.Search in Google Scholar

Roy, R., Ghosh, S. and Ghosh, A. (2018). Speckle de-noising of clinical ultrasound images based on fuzzy spel conformity in its adjacency, Applied Soft Computing73: 394–417.10.1016/j.asoc.2018.08.014Search in Google Scholar

Shan, J., Alam, S.K., Garra, B., Zhang, Y. and Ahmed, T. (2016). Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods, Ultrasound in Medicine and Biology42(4): 980–988.10.1016/j.ultrasmedbio.2015.11.01626806441Search in Google Scholar

Shi, X., Cheng, H.-D., Hu, L., Ju, W. and Tian, J. (2010). Detection and classification of masses in breast ultrasound images, Digital Signal Processing20(3): 824–836.10.1016/j.dsp.2009.10.010Search in Google Scholar

Simeone, P., Marrocco, C. and Tortorella, F. (2012). Design of reject rules for ECOC classification systems, Pattern Recognition45(2): 863–875.10.1016/j.patcog.2011.08.001Search in Google Scholar

Singh, B.K., Verma, K., Panigrahi, L. and Thoke, A. (2017a). Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: An experimental investigation in machine learning paradigm, Expert Systems with Applications90: 209–223.10.1016/j.eswa.2017.08.020Search in Google Scholar

Singh, K., Ranade, S.K. and Singh, C. (2017b). A hybrid algorithm for speckle noise reduction of ultrasound images, Computer Methods and Programs in Biomedicine148: 55–69.10.1016/j.cmpb.2017.06.00928774439Search in Google Scholar

Tesfahun, A. and Bhaskari, D.L. (2013). Intrusion detection using random forests classifier with smote and feature reduction, International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE), Pune, India, pp. 127–132.Search in Google Scholar

Tortorella, F. (2000). An optimal reject rule for binary classifiers, in F.J. Ferri et al. (Eds), Advances in Pattern Recognition, SSPR/SPR 2000, Lecture Notes in Computer Science, Vol. 1876, Springer, Berlin/Heidelberg, pp. 611–620.10.1007/3-540-44522-6_63Search in Google Scholar

Tortorella, F. (2004). Reducing the classification cost of support vector classifiers through an ROC-based reject rule, Pattern Analysis and Applications7(2): 128–143.10.1007/s10044-004-0209-2Search in Google Scholar

Wang, Z., Wang, Z., He, S., Gu, X. and Yan, Z.F. (2017). Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information, Applied Energy188: 200–214.10.1016/j.apenergy.2016.11.130Search in Google Scholar

Wu, G. and Chang, E.Y. (2005). KBA: Kernel boundary alignment considering imbalanced data distribution, IEEE Transactions on Knowledge and Data Engineering17(6): 786–795.10.1109/TKDE.2005.95Search in Google Scholar

Yassin, N.I., Omran, S., El Houby, E.M. and Allam, H. (2017). Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review, Computer Methods and Programs in Biomedicine156: 25-45.10.1016/j.cmpb.2017.12.01229428074Search in Google Scholar

Yu, H. and Ni, J. (2014). An improved ensemble learning method for classifying high-dimensional and imbalanced biomedicine data, IEEE/ACM Transactions on Computational Biology and Bioinformatics11(4): 657–666.10.1109/TCBB.2014.230683826356336Search in Google Scholar

Yu, X., Hao, X., Wan, J., Wang, Y., Yu, L. and Liu, B. (2018). Correlation between ultrasound appearance of small breast cancer and axillary lymph node metastasis, Ultrasound in Medicine & Biology44(2): 342–349.10.1016/j.ultrasmedbio.2017.09.02029150365Search in Google Scholar

Zhang, J., Lin, G., Wu, L., Wang, C. and Cheng, Y. (2015). Wavelet and fast bilateral filter based de-speckling method for medical ultrasound images, Biomedical Signal Processing and Control18: 1–10.10.1016/j.bspc.2014.11.010Search in Google Scholar

Zhou, S., Shi, J., Zhu, J., Cai, Y. and Wang, R. (2013). Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image, Biomedical Signal Processing and Control8(6): 688–696.10.1016/j.bspc.2013.06.011Search in Google Scholar

eISSN:
2083-8492
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Mathematics, Applied Mathematics