A Survey on Blood Image Diseases Detection Using Deep Learning

A Survey on Blood Image Diseases Detection Using Deep Learning

Mohamed Loey, Mukdad Rasheed Naman, Hala Helmy Zayed
Copyright: © 2020 |Pages: 15
DOI: 10.4018/IJSSMET.2020070102
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Abstract

Blood disease detection and diagnosis using blood cells images is an interesting and active research area in both the computer and medical fields. There are many techniques developed to examine blood samples to detect leukemia disease, these techniques are the traditional techniques and the deep learning (DL) technique. This article presents a survey on the different traditional techniques and DL approaches that have been employed in blood disease diagnosis based on blood cells images and to compare between the two approaches in quality of assessment, accuracy, cost and speed. This article covers 19 studies, 11 of these studies were in traditional techniques which used image processing and machine learning (ML) algorithms such as K-means, K-nearest neighbor (KNN), Naïve Bayes, Support Vector Machine (SVM), and 8 studies in advanced techniques which used DL, particularly Convolutional Neural Networks (CNNs) which is the most widely used in the field of blood image diseases detection since it is highly accurate, fast, and has the least cost. In addition, it analyzes a number of recent works that have been introduced in the field including the size of the dataset, the used methodologies, the obtained results, etc. Finally, based on the conducted study, it can be concluded that the proposed system CNN was achieving huge successes in the field whether regarding features extraction or classification task, time, accuracy, and had a lower cost in the detection of leukemia diseases.
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1. Introduction

Disease diagnosis is a critical process performed by physicians to decide on the presence or absence of a certain disease based on some criteria. In the past, blood test has played a significant role in clinically diagnosing a number of serious diseases such as leukemia, anemia, and many other hematologic diseases (Yu et al., 2017).

1.1. Blood

The main blood component is plasma, which is a clear yellow liquid that contains nutrients, proteins, electrolytes, hormones, and other blood substances. It forms about 55% of the blood. Blood cells are also blood components made in the bone marrow, divided into three types of cells which are the platelets that help stop bleeding at injury and count 150,000 to 450,000 platelets per microliter of blood, red blood cells (RBCs) also known as erythrocytes they are the most abundant cells in our bodies and count 4 to 6 million per microliter differ from men to women, and white blood cells (WBCs) also known as leucocytes, which fight infections in the body. There are five types of WBCs (neutrophils, eosinophils, basophils, lymphocytes, and monocytes) and count 4,500 to 11,000 per microliter of blood (Chaudhary et al., 2016). Table (1) shows normal blood cells count; it shows that the WBCs include many types of cells.

Table 1.
Normal blood cells count (Prinsloo et al., 2001)
Blood CellsCountTypes of Cells
Platelets150,000 to 450,000 per microliterOne type
RBCs4 to 6 millionOne type
WBCS4,500 to 11,000 per microliterneutrophils 62% of WBCs
Eosinophils 2.3%of WBCs
Basophils 0.4% of WBCs
Lymphocytes 30%of WBCs
monocytes 5.3% of WBCs

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