| Issue |
MATEC Web Conf.
Volume 154, 2018
The 2nd International Conference on Engineering and Technology for Sustainable Development (ICET4SD 2017)
|
|
|---|---|---|
| Article Number | 01041 | |
| Number of page(s) | 6 | |
| Section | Engineering and Technology | |
| DOI | https://doi.org/10.1051/matecconf/201815401041 | |
| Published online | 28 February 2018 | |
Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network
1
Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
2
Universitas Sebelas Maret, Surakarta, 57126, Indonesia
* Corresponding author: nurcahya.pradana@gmail.com
Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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