A new machine classification method applied to human peripheral blood leukocytes

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification

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

Image processing and machine learning in the morphological analysis of blood cells

Introduction: This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. Methods: The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. Results: There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Conclusion: Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.Peer ReviewedPostprint (published version

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

Classification of Leukocytes Using Meta-Learning and Color Constancy Methods

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks

An automated classification system for leukocyte morphology in acute myeloid Leukemia

Diagnosis of hematological malignancies and of acute myeloid leukemia in particular have undergone wide-ranging advances in recent years, driven by an increasingly detailed knowledge of its underlying biological and genetic mechanisms. Nevertheless, cytomorphologic evaluation of samples of peripheral blood and bone marrow is still an integral part of the routine diagnostic workup. Microscopic analysis of these samples has so far defied automation and is still mainly performed by human cytologists manually classifying and counting relevant cell populations. Access to this diagnostic modality is therefore limited by the number and availability of educated cytologists. Furthermore, its results rest on judgments of examiners, which may vary according to their education and experience, rendering rigorous quantification and standardization of the method difficult. In this thesis, an approach to cytomorphologic classification is presented that aims to harness recent advances in computational image classification for leukocyte differentiation using Deep Learning techniques that derive from the domain of Artificial Intelligence. In a first stage of the project, peripheral blood smear samples from both AML patients and controls were scanned using techniques from digital pathology. Experienced cytologists from the Laboratory of Leukemia Diagnostics at the LMU Klinikum annotated the digitized samples according to a scheme of 15 morphological categories derived from standard routine diagnostics. The resulting set of over 18,000 annotated single-cell images is the largest public database of leukocyte morphologies in leukemia available today. In a second step, the compiled dataset was used to develop a neural network that is able to classify leukocytes into the standard morphological scheme. Evaluation of network predictions show that the network performs well at the classification task for most clinically relevant categories, with an error pattern similar to that of human examiners. The network can also be employed to answer two questions of immediate clinical relevance, namely if a given single-cell image shows a blast-like cell, or if it belongs to the set of atypical cells which are not present in peripheral blood smears under physiological conditions. At these questions, the network is found to show similar and slightly better performance compared to the human examiner. These results show the potential of Deep Learning techniques in the field of hematological diagnostics and suggest avenues for their further development as a helpful tool of leukemia diagnostics.In der Diagnostik hämatologischer Erkrankungen wie der akuten myeloischen Leukämie haben sich in den vergangenen Jahren bedeutende Fortschritte ergeben, die vor allem auf einem vertieften Verständnis ihrer biologischen und genetischen Ursachen beruhen. Trotzdem spielt die zytomorphologische Untersuchung von Blut- und Knochenmarkspräparaten nach wie vor eine zentrale Rolle in der diagnostischen Aufarbeitung. Die mikroskopische Begutachtung dieser Präparate konnte bisher nicht automatisiert werden und erfolgt nach wie vor durch menschliche Befunder, die eine manuelle Differentierung und Auszählung relevanter Zelltypen vornehmen. Daher ist der Zugang zu zytomorphologischen Untersuchungen durch die Zahl verfügbarer zytologischer Befunder begrenzt. Darüber hinaus beruht die Beurteilung der Präparate auf der individuellen Einschätzung der Befunder und ist somit von deren Ausbildung und Erfahrung abhängig, was eine standardisierte und quantitative Auswertung der Morphologie zusätzlich erschwert. Ziel der vorliegenden Arbeit ist es, ein computerbasiertes System zu entwickeln, die die morphologische Differenzierung von Leukozyten unterstützt. Zu diesem Zweck wird auf in den letzten Jahren entwickelte leistungsfähige Algorithmen aus dem Bereich der Künstlichen Intelligenz, insbesondere des sogenannten Tiefen Lernens zurückgegriffen. In einem ersten Schritt des Projekts wurden periphere Blutausstriche von AML-Patienten und Kontrollen mit Methoden der digitalen Pathologie erfasst. Erfahrene Befunder aus dem Labor für Leukämiediagnostik am LMU-Klinikum München annotierten die digitalisierten Präparate und differenzierten sie in ein 15-klassiges, aus der Routinediagnostik stammendes Standardschema. Auf diese Weise wurde mit über 18,000 morphologisch annotierten Leukozyten der aktuell größte öffentlich verfügbare Datensatz relevanter Einzelzellbilder zusammengestellt. In einer zweiten Phase des Projekts wurde dieser Datensatz verwendet, um Algorithmen vom Typ neuronaler Faltungsnetze zur Klassifikation von Einzelzellbilden zu trainieren. Eine Analyse ihrer Vorhersagen zeigt dass diese Netzwerke Einzelzellbilder der meisten Zellklassen sehr erfolgreich differenzieren können. Für falsch klassifizierte Bilder ähnelt ihr Fehlermuster dem menschlicher Befunder. Neben der Klassifikation einzelner Zellen erlauben die Netzwerke auch die Beantwortung gröberer, binärer Fragestellungen, etwa ob eine bestimmte Zelle blastären Charakter hat oder zu den morphologischen Klassen gehört die in einem peripheren Blutausstrich nicht unter physiologischen Bedingungen vorkommen. Bei diesen Fragen zeigen die Netzwerke eine ähnliche und leicht bessere Leistung als der menschliche Befunder. Die Ergebnisse dieser Arbeit illustrieren das Potential von Methoden der künstlichen Intelligenz auf dem Gebiet der Hämatologie und eröffnen Möglichkeiten zu ihrer Weiterentwicklung zu einem praktischen Hilfsmittel der Leukämiediagnostik

Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201