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

Automatic recognition of different types of acute leukaemia using peripheral blood cell images

[eng] Clinical pathologists have learned to identify morphological qualitative features to characterise the different normal cells, as well as the abnormal cell types whose presence in peripheral blood is the evidence of serious haematological diseases. A drawback of visual morphological analysis is that is time consuming, requires well-trained personnel and is prone to intra-observer variability, which is particularly true when dealing with blast cells. Indeed, subtle interclass morphological differences exist for leukaemia types, which turns into low specificity scores in the routine screening. They are well-known the difficulties that clinical pathologists have in the discrimination among different blasts and the subjectivity associated with their morphological recognition. The general objective of this thesis is the automatic recognition of different types of blast cells circulating in peripheral blood in acute leukaemia using digital image processing and machine learning techniques. In order to accomplish this objective, this thesis starts with a discrimination among normal mononuclear cells, reactive lymphocytes and three types of leukemic cells using traditional machine learning techniques and hand-crafted features obtained from cell segmentation. In the second part of the thesis, a new predictive system designed with two serially connected convolutional neural networks is developed for the diagnosis of acute leukaemia. This system was proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage. Furthermore, it was evaluated for its integration in a real-clinical setting. This thesis also contributes in advancing the state of the art of the automatic recognition of acute leukaemia by providing a more realistic approach which reflects the real-life complexity of acute leukaemia diagnosis

Deep Learning System for the Automatic Classification of Normal and Dysplastic Peripheral Blood Cells as a Support Tool for the Diagnosis

[eng] Clinical pathologists identify visually many morphological features to characterize the different normal cells, as well as the abnormal cell types whose presence in peripheral blood is the evidence of serious diseases. Disadvantages of visual morphological analysis are that it is time consuming, needs expertise to perform an objective review of the smears and is prone to inter-observer variability. Also, most of the morphological descriptions are given in qualitative terms and there is a lack of quantitative measures. The general objective of this thesis is the automatic recognition of normal and dysplastic cells circulating in blood in myelodysplastic syndromes using convolutional neural networks and digital image processing techniques. In order to accomplish this objective, this work starts with the design and development of a Mysql database to store information and images from patients and the development of a first classifier of four groups of cells, using convolutional neural networks as feature extractors. Then, a high- quality dataset of around 17,000 images of normal blood cells is compiled and used for the development of a recognition system of eight groups of blood cells. In this work, we compare two transfer learning approaches to find the best to classify the different cell types. In the second part of the thesis, a new convolutional neural network model for the diagnosis of myelodysplastic syndromes is developed. This model was validated by means of a proof of concept. It is considered among the first models that have been built for diagnosis support. The final work of the thesis is the integration of two convolutional networks in a modular system for the automatic classification of normal and abnormal cells. The methodology and models developed constitute a step forward to the implementation of a modular system to recognize automatically all cell types in a real setup in the laboratory.[spa] Los especialistas de laboratorio identifican visualmente muchas características morfológicas para identificar las diferentes células normales, así como los tipos de células anormales, cuya presencia en sangre periférica es evidencia de enfermedades graves. Algunas de las desventajas del análisis morfológico visual incluyen que toma mucho tiempo, necesita experiencia para realizar una revisión objetiva de los frotis y es propenso a la variabilidad entre observadores. Además, la mayoría de las descripciones morfológicas se proporcionan en términos cualitativos. Debido a lo expuesto anteriormente, es necesario establecer medidas cuantitativas. El objetivo general de esta tesis es el reconocimiento automático de células normales y células displásicas circulantes en sangre en síndromes mielodisplásicos mediante redes neuronales convolucionales y técnicas de procesamiento digital de imágenes. Para lograr este objetivo, este trabajo comenzó con el diseño y desarrollo de una base de datos Mysql para almacenar información e imágenes de pacientes y el desarrollo de un primer clasificador de cuatro grupos de células, utilizando redes neuronales convolucionales como extractores de características. Luego, se compila un conjunto de datos de alta calidad de alrededor de 17.000 imágenes de células sanguíneas normales y se utiliza para el desarrollo de un sistema de reconocimiento de ocho grupos de células sanguíneas. En este trabajo, comparamos dos enfoques de aprendizaje por transferencia para encontrar el mejor para clasificar los diferentes tipos de células. En la segunda parte de la tesis se desarrolla un nuevo modelo de red neuronal convolucional para el diagnóstico de síndromes mielodisplásicos. Este modelo fue validado mediante prueba de concepto. Se considera uno de los primeros modelos que se han construido para apoyar el diagnóstico. El trabajo final de la tesis es la integración de dos redes convolucionales en un sistema modular para la clasificación automática de células normales y anormales. La metodología y los modelos desarrollados constituyen un paso adelante hacia la implementación de un sistema modular para reconocer automáticamente todos los tipos de células en una configuración real en el laboratorio

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images

Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models’ robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL’s transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks