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

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

BCNet: A Novel Network for Blood Cell Classification

The paper was partially supported by: Royal Society International Exchanges Cost Share Award, United Kingdom (RP202G0230); Medical Research Council Confidence in Concept Award, United Kingdom (MC_PC_17171); Hope Foundation for Cancer Research, United Kingdom (RM60G0680); British Heart Foundation Accelerator Award, United Kingdom (AA/18/3/34220); Sino-United Kingdom Industrial Fund, United Kingdom (RP202G0289); Global Challenges Research Fund (GCRF), United Kingdom (P202PF11); Guangxi Key Laboratory of Trusted Software (kx201901).Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems. Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multiclassification indexes for the evaluation of our model. Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively. Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.Royal Society International Exchanges Cost Share Award RP202G0230Medical Research Council Confidence in Concept Award, United Kingdom MC_PC_17171Hope Foundation for Cancer Research, United Kingdom RM60G0680British Heart Foundation Accelerator Award, United Kingdom AA/18/3/34220Sino-United Kingdom Industrial Fund, United Kingdom RP202G0289Global Challenges Research Fund (GCRF), United Kingdom P202PF11Guangxi Key Laboratory of Trusted Software kx20190

A PCNN Framework for Blood Cell Image Segmentation

This research presents novel methods for segmenting digital blood cell images under a Pulse Coupled Neural Network (PCNN) framework. A blood cell image contains different types of blood cells found in the peripheral blood stream such as red blood cells (RBCs), white blood cells (WBCs), and platelets. WBCs can be classified into five normal types – neutrophil, monocyte, lymphocyte, eosinophil, and basophil – as well as abnormal types such as lymphoblasts and others. The focus of this research is on identifying and counting RBCs, normal types of WBCs, and lymphoblasts. The total number of RBCs and WBCs, along with classification of WBCs, has important medical significance which includes providing a physician with valuable information for diagnosis of diseases such as leukemia. The approach comprises two phases – segmentation and cell separation – followed by classification of WBC types including detection of lymphoblasts. The first phase presents two methods based on PCNN and region growing to segment followed by a separate method that combines Circular Hough Transform (CHT) with a separation algorithm to find and separate each RBC and WBC object into separate images. The first method uses a standard PCNN to segment. The second method uses a region growing PCNN with a maximum region size to segment. The second phase presents a WBC classification method based on PCNN. It uses a PCNN to capture the texture features of an image as a sequence of entropy values known as a texture vector. First, the parameters of the texture vector PCNN are defined. This is then used to produce texture vectors for the training images. Each cell type is represented by several texture vectors across its instances. Then, given a test image to be classified, the texture vector PCNN is used to capture its texture vector, which is compared to the texture vectors for classification. This two-phase approach yields metrics based on the RBC and WBC counts, WBC classification, and identification of lymphoblasts. Both the standard and region growing PCNNs were successful in segmenting RBC and WBC objects, with better accuracy when using the standard PCNN. The separate method introduced with this research provided accurate WBC counts but less accurate RBC counts. The WBC subimages created with the separate method facilitated cell counting and WBC classification. Using a standard PCNN as a WBC classifier, introduced with this research, proved to be a successful classifier and lymphoblast detector. While RBC accuracy was low, WBC accuracy for total counts, WBC classification, and lymphoblast detection were overall above 96%

NuClick : a deep learning framework for interactive segmentation of microscopic images

Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labeled data for precise and reliable prediction. However, collecting labeled data is expensive because it often requires expert knowledge, particularly in medical imaging domain where labels are the result of a time-consuming analysis made by one or more human experts. As nuclei, cells and glands are fundamental objects for downstream analysis in computational pathology/cytology, in this paper we propose NuClick, a CNN-based approach to speed up collecting annotations for these objects requiring minimum interaction from the annotator. We show that for nuclei and cells in histology and cytology images, one click inside each object is enough for NuClick to yield a precise annotation. For multicellular structures such as glands, we propose a novel approach to provide the NuClick with a squiggle as a guiding signal, enabling it to segment the glandular boundaries. These supervisory signals are fed to the network as auxiliary inputs along with RGB channels. With detailed experiments, we show that NuClick is applicable to a wide range of object scales, robust against variations in the user input, adaptable to new domains, and delivers reliable annotations. An instance segmentation model trained on masks generated by NuClick achieved the first rank in LYON19 challenge. As exemplar outputs of our framework, we are releasing two datasets: 1) a dataset of lymphocyte annotations within IHC images, and 2) a dataset of segmented WBCs in blood smear images

Peripheral Blood Smear Analyses Using Deep Learning

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by hematologists to assess some aspects of humans’ health status. PBS analysis is prone to human errors and utilizing computer-based analysis can greatly enhance this process in terms of accuracy and cost. Recent approaches in learning algorithms, such as deep learning, are data hungry, but due to the scarcity of labeled medical images, researchers had to find viable alternative solutions to increase the size of available datasets. Synthetic datasets provide a promising solution to data scarcity, however, the complexity of blood smears’ natural structure adds an extra layer of challenge to its synthesizing process. In this thesis, we propose a method- ology that utilizes Locality Sensitive Hashing (LSH) to create a novel balanced dataset of synthetic blood smears. This dataset, which was automatically annotated during the gener- ation phase, covers 17 essential categories of blood cells. The dataset also got the approval of 5 experienced hematologists to meet the general standards of making thin blood smears. Moreover, a platelet classifier and a WBC classifier were trained on the synthetic dataset. For classifying platelets, a hybrid approach of deep learning and image processing tech- niques is proposed. This approach improved the platelet classification accuracy and macro- average precision from 82.6% to 98.6% and 76.6% to 97.6% respectively. Moreover, for white blood cell classification, a novel scheme for training deep networks is proposed, namely, Enhanced Incremental Training, that automatically recognises and handles classes that confuse and negatively affect neural network predictions. To handle the confusable classes, we also propose a procedure called "training revert". Application of the proposed method has improved the classification accuracy and macro-average precision from 61.5% to 95% and 76.6% to 94.27% respectively. In addition, the feasibility of using animal reticulocyte cells as a viable solution to com- pensate for the deficiency of human data is investigated. The integration of animal cells is implemented by employing multiple deep classifiers that utilize transfer learning in differ- ent experimental setups in a procedure that mimics the protocol followed in experimental medical labs. Moreover, three measures are defined, namely, the pretraining boost, the dataset similarity boost, and the dataset size boost measures to compare the effectiveness of the utilized experimental setups. All the experiments of this work were conducted on a novel public human reticulocyte dataset and the best performing model achieved 98.9%, 98.9%, 98.6% average accuracy, average macro precision, and average macro F-score re- spectively. Finally, this work provides a comprehensive framework for analysing two main blood smears that are still being conducted manually in labs. To automate the analysis process, a novel method for constructing synthetic whole-slide blood smear datasets is proposed. Moreover, to conduct the blood cell classification, which includes eighteen blood cell types and abnormalities, two novel techniques are proposed, namely: enhanced incremental train- ing and animal to human cells transfer learning. The outcomes of this work were published in six reputable international conferences and journals such as the computers in biology and medicine and IEEE access journals

BCNet: A Novel Network for Blood Cell Classification

Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems.Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model.Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively.Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods