A Review on Classification of White Blood Cells Using Machine Learning Models

The machine learning (ML) and deep learning (DL) models contribute to exceptional medical image analysis improvement. The models enhance the prediction and improve the accuracy by prediction and classification. It helps the hematologist to diagnose the blood cancer and brain tumor based on calculations and facts. This review focuses on an in-depth analysis of modern techniques applied in the domain of medical image analysis of white blood cell classification. For this review, the methodologies are discussed that have used blood smear images, magnetic resonance imaging (MRI), X-rays, and similar medical imaging domains. The main impact of this review is to present a detailed analysis of machine learning techniques applied for the classification of white blood cells (WBCs). This analysis provides valuable insight, such as the most widely used techniques and best-performing white blood cell classification methods. It was found that in recent decades researchers have been using ML and DL for white blood cell classification, but there are still some challenges. 1) Availability of the dataset is the main challenge, and it could be resolved using data augmentation techniques. 2) Medical training of researchers is recommended to help them understand the structure of white blood cells and select appropriate classification models. 3) Advanced DL networks such as Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN can also be used in future techniques.Comment: 23 page

Specifics of Using Image Processing Techniques for Blood Smear Analysis

The process of medical diagnosis is an important stage in the study of human health. One of the directions of such diagnostics is the analysis of images of blood smears. In doing so, it is important to use different methods and analysis tools for image processing. It is also important to consider the specificity of blood smear imaging. The paper discusses various methods for analyzing blood smear images. The features of the application of the image processing technique for the analysis of a blood smear are highlighted. The results of processing blood smear images are presented

Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell Nucleus based on Morphological Image

Hematology tests are examinations that aim to know the state of blood and its components, one of which is leukocytes. Hematologic examinations such as the number and morphology of blood generally still done manually, especially by a specialist pathologist. Despite the fact that today there is equipment that can identify morphological automatically, but for developing countries like Indonesia, it can only be done in the capital city. Low accuracy due to the differences identified either by doctors or laboratory staff, makes a great reason to use computer assistance, especially with the rapid technological developments at this time. In this paper, we will emphasize our experiment to screen leucocyte cell nucleus by identifying the contours of the cell nucleus, diameter, circumference and area of these cells based on digital image processing techniques, especially using the morphological image. The results obtained are promising for further development in the development of computer-aided diagnosis for identification of leukocytes based on a simple and inexpensive equipment

Red blood cell segmentation and classification method using MATLAB

Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very important process for early detection of related disease such as malaria and anemia before suitable follow up treatment can be proceed. Some of the human disease can be showed by counting the number of red blood cells. Red blood cell count gives the vital information that help diagnosis many of the patient’s sickness. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. This method is time-consuming and laborious. In this project an automated RBC counting is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global thresholding. Then it continues with RBCs counting by using two different algorithms which are the watershed segmentation based on distance transform, and the second one is the artificial neural network (ANN) classification with fitting application depend on regression method. Before applying ANN classification there are step needed to get feature extraction data that are the data extraction using moment invariant. There are still weaknesses and constraints due to the image itself such as color similarity, weak edge boundary, overlapping condition, and image quality. Thus, more study must be done to handle those matters to produce strong analysis approach for medical diagnosis purpose. This project build a better solution and help to improve the current methods so that it can be more capable, robust, and effective whenever any sample of blood cell is analyzed. At the end of this project it conducted comparison between 20 images of blood samples taken from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the counting method has been demonstrated

A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells

Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person's blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist's professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher

이중 합성곱 신경망을 이용한 백혈구 백분율 자동 분석 시스템에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 협동과정 바이오엔지니어링전공, 2017. 8. 김희찬.Leukocyte or white blood cell differential count is an essential examination modality of hematology laboratory in diagnosis of various blood disorders. However, it requires highly experienced hematologists for correct diagnosis from samples with inter- and intra-sample variations. Due to tedious, time and cost consuming procedure of manual differential count, there has been high demands for development of automated system. In order for it to be applicable in clinical hematology laboratories, an automated system will have to detect and classify leukocytes of different maturation stages, especially in bone marrow aspirate smears. This has been a challenging problem in computer vision, image processing, and machine learning, because of complex nature of bone marrow aspirate smear. The leukocyte has multiple maturation stages, and these maturation stages have small inter-class differences, so it is difficult to differentiate even with expert knowledge. Moreover, a problem of color, shape, and size variations among samples exists and a problem of touching cell due to high leukocyte density of bone marrow aspirate smear exists. In this dissertation, an automated leukocyte differential count system for bone marrow aspirate smear was developed to overcome problems of manual differential count and to fulfill clinically unmet needs. The system should perform the differential count with high accuracy and objectivity, and high throughput and efficiency. Moreover, it should overcome challenges of bone marrow aspirate smear. To this end, a large dataset of bone marrow smear was collected for development of a detection and a classification algorithms. Watershed transformation and saliency map were utilized for single-leukocyte detection, and the dual-stage convolutional neural network that learns global and local features of complex leukocyte maturation stages was proposed for classification. Lastly, a probability guidance algorithm was proposed for integration of detection and classification algorithms. The performance of proposed system was assessed with ten leukocyte maturation stages of myeloid and erythroid series in bone marrow aspirate smears. Total of 200 large (1388 × 1040) digital images of bone marrow aspirate smears and 2,323 small (96 × 96) single leukocyte digital images were collected. The proposed system showed a state-of-the-art performance. It achieved an average detection accuracy of 96.09% and an average classification accuracy of 97.06%, and it was able to differential count 100 leukocytes in 4 to 5 seconds. This proposes a new paradigm in diagnosis of blood disorder and showed a potential of deep learning, especially the convolutional neural network, in medical image processing. The proposed system is expected to increase the total number of analyzed leukocytes in a sample, which will provide more statistically reliable information of a patient for diagnosis.Chapter 1 Introduction 1 1.1. Introduction to Hematology 2 1.2. Introduction to Convolutional Neural Network 12 1.3. Thesis Objectives 16 Chapter 2 Leukocyte Data Collection 19 2.1. Sample Preparation and Acquisition 20 2.2. Dataset Collection and Preparation 23 Chapter 3 Leukocyte Classification 27 3.1. Introduction 28 3.2. Methods 36 3.2.1. Data Collection and Preparation 36 3.2.2. Data Oversampling and Augmentation 38 3.2.3. Convolutional Neural Network Architecture and Dual-stage Convolutional Neural Network 40 3.2.4. Convolutional Neural Network Training 43 3.2.5. Implementation 46 3.2.6. Evaluation Metrics 46 3.3. Results and Discussion 48 3.4. Conclusion 66 Chapter 4 Implementation of Automated Leukocyte Differential Count System 67 4.1. System Overview 68 4.2. Leukocyte Detection 70 4.2.1. Introduction 70 4.2.2. Detection Algorithm 75 4.2.3. Experimental Setup and Evaluation 81 4.2.4. Results and Discussion 82 4.3. Automated Leukocyte Differential Count System 92 4.3.1. Implementation of Detection and Classification Algorithms 92 4.3.2. Graphical User Interface Design 93 4.3.3. Probability Guidance Algorithm 95 4.3.4. Experimental Setup and Evaluation 97 4.3.5. Results and Discussion 98 4.4. Conclusion 102 Chapter 5 Thesis Summary and Future Work 104 5.1 Thesis Summary and Contributions 105 5.2 Future Work 109 Bibliography 115 Abstract in Korean 122Docto

Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1

Red blood cell image enhancement techniques for cells with overlapping condition

This paper proposes an automatic algorithm consists of techniques that can separateoverlapping RBC for enhancing RBC image to improve counting precision. The proposedalgorithm is able to automatically count RBC of colour blood smear images based on thequality or challenging conditions of the images such as poor illumination of blood smear andmost importantly overlapping RBC. The algorithm comprises of two RBC segmentation thatcan be selected based on the image quality, circle mask technique and grayscale blood smearimage processing. Detail explanations including experimental results that show the effectiveness of the proposed techniques are described in this paper. The proposed algorithm has been successfully detect and separate agglomerate RBC with 96% accuracy. Algorithmvalidation was verified with RBC distribution area and confusion matrix.Keywords: red blood cell (RBC); complete blood count (CBC); automated segmentation;overlap RBC