1. Libbrecht MW, Noble WS. Machine Learning in Genetics and Genomics. Nature Reviews 2015; 16(6): 321-2. [
DOI:10.1038/nrg3920]
2. Hosseini MM, Safdari R, Shahmoradi L, et al. Better Diagnosis of Acute Appendicitis by Using Artificial Intelligence. Iran South Med J 2017; 20 (4): 339-48.
3. Prince JL, Links JM. Basic Imaging Principles. In: Horton MJ. Medical Imaging Signals and Systems. 2nd ed. Upper Saddle River, N.J.: Pearson. 2015. 5-13.
4. Gurcan MN, Boucheron LE, Can A, et al. Histopathological Image Analysis: A Review. IEEE Reviews in Biomedical Engineering 2009; 2: 147-71. [
DOI:10.1109/RBME.2009.2034865]
5. Mohapatra S, Patra D, Satpathy S. An Ensemble Classifier System for Early Diagnosis of Acute Lymphoblastic Leukemia in Blood Microscopic Images. Neural Computing and Applications 2014; 24(7-8): 1887-1904. [
DOI:10.1007/s00521-013-1438-3]
6. Sabino DMU, da Fontoura Costa Ldf, Rizzatti G, et al. A Texture Approach to Leukocyte Recognition. Real-Time Imaging 2004; 10(4): 205-16. [
DOI:10.1016/j.rti.2004.02.007]
7. Chassery JM, Garbay C. An Iterative Segmentation Method Based on Contextual Color and Shape Criterion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1984; 6(6): 794-800. [
DOI:10.1109/TPAMI.1984.4767603]
8. Jiang K, Liao QM, Dai SY. A Novel White Blood Cell Segmentation Scheme using Scale-Space Filtering and Watershed Clustering. In Machine Learning and Cybernetics 2002; 32(1): 48-53.
9. Rezatofighi, SH, Soltanian-Zadeh H. Automatic Recognition of Five Types of White Blood Cells in Peripheral Blood. Computerized Medical Imaging and Graphics 2011; 35(4): 333-43 [
DOI:10.1016/j.compmedimag.2011.01.003]
10. Theera-Umpon N, Dhompongsa S. Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification. IEEE Transactions on Information Technology in Biomedicine 2007; 11(3): 353-9. [
DOI:10.1109/TITB.2007.892694]
11. Farjam R, Soltanian‐Zadeh H, Jafari Khouzani K, et al. An Image Analysis Approach for Automatic Malignancy Determination of Prostate Pathological Images. Cytometry B Clin Cytom 2007; 72(4): 227-40. [
DOI:10.1002/cyto.b.20162]
12. Long X, Cleveland WL, Yao YL. A New Preprocessing Approach for Cell Recognition. IEEE Tranactions on Information Technology in Biomedicine 2005; 9(3): 407-12 [
DOI:10.1109/TITB.2005.847502]
13. Theera-Umpon N, Gader PD. System-Level Training of Neural Networks for Counting White Blood Cells. IEEE Transactions on Systems, Man, and Cybernetics 2002; 32(1): 48-53. [
DOI:10.1109/TSMCC.2002.1009139]
14. Shitong W, Min W. A New Detection Algorithm (NDA) based on Fuzzy Cellular Neural Networks for White Blood Cell Detection. IEEE Tranactions on Information Technology in Biomedicine 2006; 10(1): 5-10. [
DOI:10.1109/TITB.2005.855545]
15. Kashefpur M, Kafieh R, Jorjandi S, et al. Isfahan MISP Dataset. Journal of Medical Signals and Sensors 2017; 7(1): 43-8. [
DOI:10.4103/2228-7477.199157]
16. Sarrafzadeh O, Rabbani H, Talebi A, et al. Selection of the Best Features for Leukocytes Classification in Blood Smear Microscopic Images. In Proceedings of the SPIE Medical Imaging. International Society for Optics and Photonics, SPIE Medical Imaging 2014: Digital Pathology, SPIE 9041, SPIE, San Diego, California, USA. [
DOI:10.1117/12.2043605]
17. Gonzalez RC, Woods RE. Intensity Transformations and Spatial Filtering. In: Horton MJ. Digital Image Processing. 3rd ed. Upper Saddle River, N.J.: Prentice Hall 2008. 152-157.
18. Zhang YJ. A Survey on Evaluation Methods for Image Segmentation. Pattern Recognition 1996; 29(8): 1335-46. [
DOI:10.1016/0031-3203(95)00169-7]
19. Suykens JAk, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters 1999; 9(3): 293-300. [
DOI:10.1023/A:1018628609742]
20. Guo Y, Liu Y, Oerlemans A, et al. Deep Learning for Visual Undertanding: A Review. Neurocomputing 2016; 187: 27-48. [
DOI:10.1016/j.neucom.2015.09.116]
21. LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature 2015; 521: 436-44. [
DOI:10.1038/nature14539]
22. Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. International Conference on Machine Learning 2015; 37: 448-56.
23. Srivastava N, Hinton GE, Krizhever A, et al. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 2014; 15: 1929-58.
24. Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks. 2015; 61: 85-117. [
DOI:10.1016/j.neunet.2014.09.003]
25. Hartigan JA, Wong MA. Algorithm AS 136: A k-means Clustering Algorithm. Journal of the Royal Statistical Society. 1979; 28(1): 100-8. [
DOI:10.2307/2346830]
26. Krizhevski A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 2012; 25.
27. Abadi M, Agarwal A, Barham P, et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv:1603.04467. 2016.