FAB classification of acute leukemia using an ensemble of neural networks
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RESEARCH PAPER
FAB classification of acute leukemia using an ensemble of neural networks Jyoti Rawat1 · Jitendra Virmani2 · Annapurna Singh3 · H. S. Bhadauria3 · Indrajeet Kumar4 · J. S. Devgan5 Received: 22 August 2019 / Revised: 22 May 2020 / Accepted: 3 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Acute leukemia is the most frequently occurring malignancy present in human blood and a kind of liquid cancer. This hematological disorder can impinge on bone marrow and lymphatic system. Accordingly, a computer-aided classification system is proposed for French–American–British classification of Acute Leukemia using an ensemble of neural networks which is validated on 180 microscopic blood images taken from online benchmark dataset. As per the requirement of pathologists in real-life examination scenario various objectives are formulated as (i) correct nucleus segmentation in blood cell image, (ii) correct classification of FAB classes of acute leukemia (L1, L2, L3, M2, M3, and M5). To accomplish these research objectives the proposed method consist of segmentation section, feature extraction section, feature pruning section and classification section. The classification of the proposed method consists of two subsections as subsection1 is comprised of single six class PCA based neural network as PCA-NN0 (L1/L2/L3/M2/M3/M5) and subsection 2 contains an ensemble of 15 binary PCA based neural network classifiers as PCA-NN1 (L1/L2), PCA-NN2(L1/L3), PCA-NN3(L1/M2), PCA-NN4(L1/M3), PCA-NN5(L1/M5), PCA-NN6(L2/L3), PCA-NN7(L2/M2), PCA-NN8(L2/M3), PCA-NN9(L2/M5), PCA-NN10(L3/M2), PCA-NN11(L3/M3), PCA-NN12(L3/M5), PCA-NN13(M2/M3), PCA-NN14(M2/M5), PCA-NN15(M3/M5). The achieved accuracy for experiment 1 is 86.4% using PCA-NN0. The output of two most plausible classes predicted by PCA-NN0 is passed to other binary PCA based neural network i.e. PCA-NN1 to PCA-NN15. After passing all the test images to subsection 2, the achieved accuracy is 94.2% from the exhaustive experiment 2. The outcome of the work verifies the capabilities of computer-aided classification system to substitute the conventional diagnostic systems. Keywords Acute leukemia · Leukemia classification · Feature extraction · Ensemble neural network
1 Introduction The assessment of the peripheral blood smear is an imperative indicator of hematological and other unusual conditions that influence the body of a human being. In general, when most people hear the word cancer, then they usually think of * Jyoti Rawat [email protected] 1
DIT University, Dehradun, Uttarakhand, India
2
CSIR-Central Scientific Instruments Organization, Chandigarh, India
3
G.B. Pant Institute of Engineering and Technology, Pauri Garhwal, UK, India
4
Graphic Era Hill University, Dehradun, Uttarakhand, India
5
M. M. Institute of Medical Sciences and Research, Solan, HP, India
a mass that is formed by rapid division of a group of cells. This mass can be seen by eyes and can be considered as the root of a problem that needs to b
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