Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification

Engineering Applications of Artificial Intelligence - Tập 72 - Trang 415-422 - 2018
Luis H. S. Vogado1, Rodrigo Veras1, Flávio Araújo1, Romuere Silva1, Kelson Aires1
1Federal University of Piauí, Teresina, Brazil

Tóm tắt

Từ khóa


Tài liệu tham khảo

Agaian, 2018, A new acute leukaemia-automated classification system, Comput. Meth. Biomech. Biomed. Eng.: Imaging Vis., 6, 303

Aha, 1991, Instance-based learning algorithms, Mach. Learn., 6, 37, 10.1007/BF00153759

Athiwaratkun, B., Kang, K., 2015. Feature representation in convolutional neural networks, CoRR abs/1507.02313. arXiv:1507.02313.

Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324

Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L., 2015. Land use classification in remote sensing images by convolutional neural networks, pp. 1–11, CoRR abs/1508.00092.

Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A., 2014. Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference, arXiv:1405.3531.

chen He, 1990, Texture unit, texture spectrum, and texture analysis, IEEE Trans. Geosci. Remote Sens., 28, 509, 10.1109/TGRS.1990.572934

Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018

Friedman, 1997, Bayesian network classifiers, Mach. Learn., 29, 131, 10.1023/A:1007465528199

Guyon, 2006, An introduction to feature extraction, 1, 10.1007/978-3-540-35488-8_1

Hall, 2009, The weka data mining software: An update, SIGKDD Explor. Newslett., 11, 10, 10.1145/1656274.1656278

Haralick, 1973, Texture features for image classification, IEEE Trans. Syst. Man Cybern., 3, 610, 10.1109/TSMC.1973.4309314

Jia, 2014, Caffe: Convolutional architecture for fast feature embedding, 675

Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097

Kumar, 2016, An ensemble of fine-tuned convolutional neural networks for medical image classification, IEEE J. Biomed. Health Inform., 21, 1

Labati, R.D., Piuri, V., Scotti, F., 2011. All-idb: The acute lymphoblastic leukemia image database for image processing. In: 18th IEEE International Conference on Image Processing, ICIP, pp. 2045–2048.

Lecun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539

Litjens, G.J.S., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sánchez, C.I., 2017. A survey on deep learning in medical image analysis, CoRR abs/1702.05747.

Madhukar, M., Agaian, S., Chronopoulos, A.T., 2012. New decision support tool for acute lymphoblastic leukemia classification. In: Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, pp. 8295 –8295 –12.

Mohapatra, 2014, An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images, Neural Comput. Appl., 24, 1887, 10.1007/s00521-013-1438-3

Mohaprata, S., Samanta, S.S., Patra, D., Satpathi, S., 2011. Fuzzy based blood image segmentation for automated leukemia detection. In: 2011 International Conference on Devices and Communications, pp. 1–5.

Neoh, 2015, An intelligent decision support system for leukaemia diagnosis using microscopic blood images, Sci. Rep., 5, 1

Omid Sarrafzadeh, 2014, Selection of the best features for leukocytes classification in blood smear microscopic images, Proc. SPIE, 9041, 9041

Patel, 2015, Automated leukaemia detection using microscopic images, Procedia Comput. Sci., 58, 635, 10.1016/j.procs.2015.08.082

Pentland, 1984, Fractal-based description of natural scenes, IEEE Trans. Pattern Anal. Mach. Intell., PAMI-6, 661, 10.1109/TPAMI.1984.4767591

Popescu, 2009, Multilayer perceptron and neural networks, WSEAS Trans. Circuits Syst., 8, 579

Powers, 2011, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, Int. J. Mach. Learn. Technol., 2, 37

Putzu, 2014, Leucocyte classification for leukaemia detection using image processing techniques, Artif. Intell. Med., 62, 179, 10.1016/j.artmed.2014.09.002

Rawat, J., Singh, A., Bhadauria, H.S., Virmani, J., 2015. Computer aided diagnostic system for detection of leukemia using microscopic images. In: 4th International Conference on Eco-Friendly Computing and Communication Systems, pp. 748–756.

Rollins-Raval, 2012, Experience with cellavision dm96 for peripheral blood differentials in a large multi-center academic hospital system, J. Pathol. Inform., 3, 1

Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis. (IJCV), 115, 211, 10.1007/s11263-015-0816-y

Sarrafzadeh, 2015, Nucleus and cytoplasm segmentation in microscopic images using K means clustering and region growing, Adv. Biomed. Res., 79

Sarrafzadeh, 2015, Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation, 3339

Schwenker, 2001, Three learning phases for radial-basis-function networks, Neural Netw., 14, 439, 10.1016/S0893-6080(01)00027-2

Shin, 2016, Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Med. Imaging, 35, 1285, 10.1109/TMI.2016.2528162

Singh, 2017, Design of new architecture to detect leukemia cancer from medical images, Int. J. Appl. Eng. Res., 11, 7087

Singhal, 2016, Texture features for the detection of acute lymphoblastic leukemia, 535

Tajbakhsz, 2016, Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 1299, 10.1109/TMI.2016.2535302

Vincent, 2015, Acute lymphoid leukemia classification using two-step neural network classifier, 1

Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H., 2016. Deep learning for identifying metastatic breast cancer, ArXiv E-Prints.