Open Access

ARTICLE

Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

Abdullah Alqahtani¹, Shtwai Alsubai¹, Mohemmed Sha^1,*, Muhammad Attique Khan², Majed Alhaisoni³, Syed Rameez Naqvi²

1 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Saudi Arabia
2 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
3 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Mohemmed Sha. Email:

Computer Systems Science and Engineering 2023, 46(1), 107-123. https://doi.org/10.32604/csse.2023.030727

Received 31 March 2022; Accepted 19 May 2022; Issue published 20 January 2023

Abstract

White blood cells (WBC) are immune system cells, which is why they are also known as immune cells. They protect the human body from a variety of dangerous diseases and outside invaders. The majority of WBCs come from red bone marrow, although some come from other important organs in the body. Because manual diagnosis of blood disorders is difficult, it is necessary to design a computerized technique. Researchers have introduced various automated strategies in recent years, but they still face several obstacles, such as imbalanced datasets, incorrect feature selection, and incorrect deep model selection. We proposed an automated deep learning approach for classifying white blood disorders in this paper. The data augmentation approach is initially used to increase the size of a dataset. Then, a Darknet-53 pre-trained deep learning model is used and fine-tuned according to the nature of the chosen dataset. On the fine-tuned model, transfer learning is used, and features engineering is done on the global average pooling layer. The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique. Following that, machine learning classifiers are used to classify the selected best features for final classification. The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.

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Keywords

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