Journal
SOFT COMPUTING
Volume 24, Issue 11, Pages 8163-8172Publisher
SPRINGER
DOI: 10.1007/s00500-019-04383-8
Keywords
White blood cell detection; Deep learning; Convolutional neural networks; Extreme learning machine; MRMR algorithm
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White blood cell (WBC) test is used to diagnose many diseases, particularly infections, ranging from allergies to leukemia. A physician needs clinical experience to detect and classify the amount of WBCs in human blood. WBCs are divided into four subclasses: eosinophils, lymphocytes, monocytes, and neutrophils. In the present study, pre-trained architectures, namely AlexNet, VGG-16, GoogleNet, and ResNet, were used as feature extractors. The features obtained from the last fully connected layers of these architectures were combined. Efficient features were selected using the minimum redundancy maximum relevance method. Finally, unlike classical convolutional neural network (CNN) architectures, the extreme learning Machine (ELM) classifier was used in the classification stage thanks to the efficient features obtained from CNN architectures. Experimental results indicated that efficient CNN features yielded satisfactory results in a shorter execution time via ELM classification with an accuracy rate of 96.03%.
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