Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

• Published in: MATEC Web of Conferences Vol. 154; p. 1041
• Main Authors: Harjoko, Agus, Ratnaningsih, Tri, Suryani, Esti, Wiharto, Palgunadi, Sarngadi, Prakisya, Nurcahya Pradana Taufik
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2018
• Subjects: Artificial neural networks; Back propagation; Classification; Contours; Digital imaging; Feature extraction; Image processing; Image segmentation; Leukemia; Leukocytes; Neural networks; Nuclei (cytology); Parameter sensitivity; Shape; Training
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/201815401041

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.