Page 91 - Big Data Analytics for Intelligent Healthcare Management
P. 91

84      CHAPTER 4 TRANSFER LEARNING AND SUPERVISED CLASSIFIER






             REFERENCES
              [1] Who.int, Breast Cancer, Available from: http://www.who.int/cancer/prevention/diagnosis-screening/breast-
                 cancer/en/, 2015. (Accessed 12 February 2018).
              [2] Mayoclinic.org, Breast Cancer–Diagnosis and Treatment–Mayo Clinic, Available from: https://www.
                 mayoclinic.org/diseases-conditions/breast-cancer/diagnosis-treatment/drc-20352475, 2018. (Accessed 10
                 June 2018).
              [3] F.A. Spanhol, L.S. Oliveira, C. Petitjean, L. Heutte, A dataset for breast cancer histopathological image
                 classification. IEEE Trans. Biomed. Eng. 63 (7) (July 2016) 1455–1462, https://doi.org/10.1109/
                 TBME.2015.2496264.
              [4] F.A. Spanhol, L.S. Oliveira, C. Petitjean, L. Heutte, Breast cancer histopathological image classification
                 using convolutional neural networks. in: 2016 International Joint Conference on Neural Networks (IJCNN),
                 Vancouver, BC, 2016, pp. 2560–2567, https://doi.org/10.1109/IJCNN.2016.7727519.
              [5] F.A. Spanhol, L.S. Oliveira, P.R. Cavalin, C. Petitjean, L. Heutte, Deep features for breast cancer histopath-
                 ological image classification. in: 2017 IEEE International Conference on Systems, Man, and Cybernetics
                 (SMC), Banff, AB, 2017, pp. 1868–1873, https://doi.org/10.1109/SMC.2017.8122889.
              [6] H. Youh, G. Rumbe, Comparative study of classification techniques on breast cancer FNA biopsy data, Int. J.
                 Int. Multimed. Artif. Intell. 1 (2010) 6–12.
              [7] M. Meraliyev, M. Zhaparov, K. Artykbayev, Choosing best machine learning algorithm for breast cancer
                 prediction, Int. J. Adv. Sci. Eng. Technol. 5 (3) (2017) 50–54.
              [8] L. Shen, End-to-End Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design,
                 eprint arXiv:1708.09427, 2017.
              [9] A. Osareh, B. Shadgar, Machine learning techniques to diagnose breast cancer. in: 2010 5th International
                 Symposium on Health Informatics and Bioinformatics, Antalya, 2010, pp. 114–120, https://doi.org/
                 10.1109/HIBIT.2010.5478895.
             [10] Agarap, Abien Fred, On Breast Cancer Detection: An Application of Machine Learning Algorithms on the
                 Wisconsin Diagnostic Dataset, https://doi.org/10.1145/3184066.3184080, arXiv:1711.07831, 2017.
             [11] J. Kriti Virmani, N. Dey, V. Kumar, PCA-PNN and PCA-SVM based CAD systems for breast density clas-
                 sification, in: Applications of Intelligent Optimization in Biology and Medicine, 2016. https://dblp.uni-trier.
                 de/db/series/isrl/isrl96.html.
             [12] L. Saba, N. Dey, A.S. Ashour, et al., Automated stratification of liver disease in ultrasound: an online accurate
                 feature classification paradigm, Comput. Methods Prog. Biomed. 130 (2016) 118–134.
             [13] N. Dey, A. Ashour, Classification and Clustering in Biomedical Signal Processing, first ed., IGI Global,
                 Hershey, PA, USA, 2016.
             [14] S. Cheriguene, N. Azizi, N. Zemmal, N. Dey, H. Djellali, N. Farah, Optimized tumor breast cancer classi-
                 fication using combining random subspace and static classifiers selection paradigms, in: A.E. Hassanien,
                 C. Grosan, T.M. Fahmy (Eds.), Applications of Intelligent Optimization in Biology and Medicine, Intelligent
                 Systems Reference Library, 96, Springer, Cham, 2016, pp. 289–307.
             [15] N. Zemmal, N. Azizi, N. Dey, M. Sellami, Adaptive semi supervised support vector machine semi supervised
                 learning with features cooperation for breast cancer classification, J. Med. Imaging Health Inf. 6 (1) (2016)
                 53–62.
             [16] S. Kamal, N. Dey, S.F. Nimmy, S.H. Ripon, N.Y. Ali, A.S. Ashour, F. Shi, Evolutionary framework for
                 coding area selection from cancer data, Neural Comput. & Applic. 29 (4) (2018) 1015–1037.
             [17] A. Bhattacherjee, S. Roy, S. Paul, P. Roy, N. Kausar, N. Dey, Classification approach for breast cancer
                 detection using back propagation neural network: A study, in: Biomedical Image Analysis and Mining
                 Techniques for Improved Health Outcomes, IGI Global, 2016, pp. 210–221.
             [18] H. Das, B. Naik, H.S. Behera, Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM)
                 Approach, Progress in Computing, Analytics and Networking, Springer, Singapore, 2018, pp. 539–549.
   86   87   88   89   90   91   92   93   94   95   96