Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images
作者： Roopa B. Hegde , Keerthana Prasad , Harishchandra Hebbar , Brij Mohan Kumar Singh
DOI: 10.1016/J.BBE.2019.01.005
关键词: Image processing 、 Peripheral blood 、 Neural network classifier 、 Convolutional neural network 、 Blood cell analysis 、 Abnormal cell 、 Transfer of learning 、 Pattern recognition 、 Artificial intelligence 、 Computer science 、 Deep learning
摘要: … image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network … In this paper, we present automated classification of WBCs …
elsevier.com
sci-hub.se 参考文章(40)
N. Sinha, A.G. Ramakrishnan, Automation of differential blood count ieee region 10 conference. ,vol. 2, pp. 547- 551 ,(2003) , 10.1109/TENCON.2003.1273221
Jaroonrut Prinyakupt, Charnchai Pluempitiwiriyawej, Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers Biomedical Engineering Online. ,vol. 14, pp. 63- 63 ,(2015) , 10.1186/S12938-015-0037-1
Seyed Hamid Rezatofighi, Hamid Soltanian-Zadeh, Automatic recognition of five types of white blood cells in peripheral blood Computerized Medical Imaging and Graphics. ,vol. 35, pp. 333- 343 ,(2011) , 10.1016/J.COMPMEDIMAG.2011.01.003
Chen Pan, Dong Sun Park, Yong Yang, Hyouck Min Yoo, Leukocyte image segmentation by visual attention and extreme learning machine Neural Computing and Applications. ,vol. 21, pp. 1217- 1227 ,(2012) , 10.1007/S00521-011-0522-9
Subrajeet Mohapatra, Dipti Patra, Sanghamitra Satpathy, An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images Neural Computing and Applications. ,vol. 24, pp. 1887- 1904 ,(2014) , 10.1007/S00521-013-1438-3
Sedat Nazlibilek, Deniz Karacor, Tuncay Ercan, Murat Husnu Sazli, Osman Kalender, Yavuz Ege, Automatic segmentation, counting, size determination and classification of white blood cells Measurement. ,vol. 55, pp. 58- 65 ,(2014) , 10.1016/J.MEASUREMENT.2014.04.008
H.T. Madhloom, S.A. Kareem, H. Ariffin, A.A. Zaidan, H.O. Alanazi, B.B. Zaidan, An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold Journal of Applied Sciences. ,vol. 10, pp. 959- 966 ,(2010) , 10.3923/JAS.2010.959.966
Hoo-Chang Shin, M. R. Orton, D. J. Collins, S. J. Doran, M. O. Leach, Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 35, pp. 1930- 1943 ,(2013) , 10.1109/TPAMI.2012.277
Qingli Li, Yiting Wang, Hongying Liu, Xiaofu He, Dongrong Xu, Jianbiao Wang, Fangmin Guo, Leukocyte cells identification and quantitative morphometry based on molecular hyperspectral imaging technology Computerized Medical Imaging and Graphics. ,vol. 38, pp. 171- 178 ,(2014) , 10.1016/J.COMPMEDIMAG.2013.12.008
Humayun Irshad, Antoine Veillard, Ludovic Roux, Daniel Racoceanu, Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential IEEE Reviews in Biomedical Engineering. ,vol. 7, pp. 97- 114 ,(2014) , 10.1109/RBME.2013.2295804