A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine

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%.