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Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images
Abstract
The early identification of acute lymphoblastic leukemia symptoms in patients can greatly increase the probability of recovery. Nowadays the leukemia disease can be identified by automatic specific tests such as Cytogenetics and Immunophenotyping and morphological cell classification made by experienced operators observing blood/marrow microscope images. Those methods are not included into large screening programs and are applied only when typical symptoms appears in normal blood analysis. The Cytogenetics and Immunophenotyping diagnostic methods are currently preferred for their great accuracy with respect to the method of blood cell observation which presents undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operator's capabilities and tiredness. Conversely, the morphological analysis just requires an image -not a blood sample- and hence is suitable for low-cost and remote diagnostic systems. The presented paper shows the effectiveness of an automatic morphological method to identify the Acute Lymphocytic Leukemia by peripheral blood microscope images. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it selects the lymphocyte cells (the ones interested by acute leukemia), it evaluates morphological indexes from those cells and finally it classifies the presence of the leukemia.
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Cited by
Classification of acute lymphoblastic leukemia using deep learning
Rehman, A., Abbas, N., Saba, T., Rahman, S. I. U., Mehmood, Z., & Kolivand, H. (2018) · Microscopy Research and Technique · doi:10.1002/jemt.23139 · Cited by 328
Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks
Matek, C., Schwarz, S., Spiekermann, K., & Marr, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0101-9 · Cited by 281
Identification of ischemic heart disease via machine learning analysis on magnetocardiograms
Tantimongcolwat, T., Naenna, T., Isarankura-Na-Ayudhya, C., Embrechts, M. J., & Prachayasittikul, V. (2008) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2008.04.009 · Cited by 57
Bigorra, L., Merino, A., Alférez, S., & Rodellar, J. (2017) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.22024 · Cited by 53
Metadata
- Authors
- Scotti, F.
- Venue
- CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005.
- Publisher
- IEEE
- Published
- 2005-10-24
- Pages
- 96-101
- OpenAlex
- W2128074360
- Cited by
- 195
- References
- 0
- FWCI
- 1.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Scotti, F. (2005). Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005. https://doi.org/10.1109/cimsa.2005.1522835
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