Open Access

ARTICLE

A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells Classification

by Khaled A. Fathy, Humam K. Yaseen^*, Mohammad T. Abou-Kreisha, Kamal A. ElDahshan

Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, 11884, Egypt

* Corresponding Author: Humam K. Yaseen. Email:

Computers, Materials & Continua 2023, 75(1), 1527-1545. https://doi.org/10.32604/cmc.2023.036322

Received 26 September 2022; Accepted 14 December 2022; Issue published 06 February 2023

Abstract

Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep learning architectures. Also, Binary Border Collie Optimization (BBCO) is introduced as an updated version of Border Collie Optimization (BCO) for feature reduction based on maximizing classification accuracy. The proposed computer aid diagnosis tool merges transfer deep learning ResNet101, BBCO feature reduction, and Support Vector Machine (SVM) classifier to form a hybrid model ResNet101-BBCO-SVM an accurate and fast model for classifying WBCs. As a result, the ResNet101-BBCO-SVM scores the best accuracy at 99.21%, compared to recent studies in the benchmark. The model showed that the addition of the BBCO algorithm increased the detection accuracy, and at the same time, decreased the classification time consumption. The effectiveness of the ResNet101-BBCO-SVM model has been demonstrated and beaten in reasonable ratios in recent literary studies and end-to-end transfer learning of pre-trained models.

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Keywords

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