Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Improving blood cells classification in peripheral blood smears using enhanced incremental training

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.26% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier’s performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split the data, which not only aids in reducing overfitting but also helps in generalizing the model.

Blood cell detection considers a gold standard key in diagnosing blood disease and producing automatic reports to hematologists and doctors. Blood cell detection is a challenging task due to non-illumination level, high number of overlapped cells per image, variations in cell densities among platelets, white blood cells and red blood cells, and the variety of staining process. Traditional procedure of blood cell detection requires pathologist effort and time. In computer aided diagnosis, machine learning and deep learning techniques become the practical way to automate the procedure of diagnosing, classify microscopic blood cells, and increase the accuracy and speed of the procedure. This paper provides a review of the detection and classification of blood cell, including red blood cells, white blood cells and platelets and their characteristics using machine learning techniques. We also have detailed the dataset of microscope blood cell. We have divided the previous works into four categories based on the output of the models, including pre-processing, segmentation, feature extraction and classification. Then, we discuss the challenges that face these methods and suggest the potential future techniques.

The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification. Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance. The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set. Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

White Blood Cell (WBC) classification plays a crucial role in diagnosing various hematological disorders and infections. Automated WBC classification using deep learning techniques has gained significant attention due to its efficiency and accuracy. This conference paper presents a study on the application of the VGG-19 deep learning model for WBC classification. The research highlights the methodology, experimental results, advantages, challenges, and future research directions in utilizing VGG-19 for medical image analysis, particularly in hematology. White Blood Cell (WBC) classification plays a crucial role in diagnosing various hematological disorders, including leukemia and infections. Traditional manual methods of WBC classification are time-consuming and prone to human error. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have demonstrated remarkable accuracy in medical image classification. This study explores the use of the VGG-19 architecture for automated WBC classification to enhance diagnostic efficiency and accuracy. The proposed model is trained on a labeled dataset of WBC images, leveraging the deep hierarchical features of VGG-19 to classify different WBC types, such as neutrophils, eosinophils, basophils, monocytes, and lymphocytes. Transfer learning is employed to fine-tune the pre-trained VGG-19 model, improving its performance on the specialized medical dataset. The model's performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the VGG-19-based approach achieves high classification accuracy, outperforming conventional machine learning methods.

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep learning-based SARS-CoV-2 diagnosis and explores potential future directions and methodological recommendations for research and advancement.

Accurate classification of sperm morphology is fundamental in evaluating male fertility and supporting reproductive health diagnostics. However, conventional manual assessment methods are often subjective, labor-intensive, and prone to inconsistencies. To address these limitations, this study presents a deep learning-based framework that integrates the Xception convolutional neural network with a Convolutional Block Attention Module (CBAM) to enhance automated classification performance. The model is trained and evaluated on the Sperm Morphology Image Dataset (SMIDS), comprising 3,000 high-resolution microscopic images categorized into Abnormal Sperm, Normal Sperm, and Non-Sperm classes. By leveraging transfer learning from ImageNet and incorporating both spatial and channel attention mechanisms, the model selectively emphasizes diagnostically salient features while suppressing irrelevant information. Experimental results demonstrate high generalization capability, achieving a test accuracy of 96.2%, with macro-averaged precision, recall, and F1-score of 95.0%, 95.3%, and 95.1%, respectively. The average Area Under the ROC Curve (AUC) reached 0.99 across all classes. Additional analyses, including confusion matrix evaluation, ROC and precision–recall curves, and class-wise performance metrics, confirm the model’s robustness and clinical reliability. Qualitative assessments further validate its discriminative power in real-world scenarios. This research underscores the potential of attention-augmented convolutional architectures in medical image analysis and offers a scalable, interpretable, and efficient tool for sperm morphology assessment in clinical and laboratory environments. Keywords: Sperm Morphology Classification, Medical Image Analysis, Xception Network, CBAM Attention Mechanism, Deep Learning, SMIDS Dataset, Male Infertility Diagnosis, Computer-Aided Diagnosis.

The problem of identifying and counting blood cells within the blood smear is of both theoretical and practical interest. The differential counting of blood cells provides invaluable information to pathologist for diagnosis and treatment of many diseases. In this paper we propose an efficient hierarchical blood cell image identification and classification method based on multi-class support vector machine. In this automated process, segmentation and classification of blood cells are the most important stages. We segment the stained blood cells in digital microscopic images and extract the geometric features for each segment to identify and classify the different types of blood cells. The experimental results are compared with the manual results obtained by the pathologist, and demonstrate the effectiveness of the proposed method.

Artificial intelligence (AI) has emerged as a revolutionary tool in hematology, capable of analyzing, interpreting, and integrating complex biological, morphological, and genomic data with enhanced precision and speed. This review aims to explore the applications of AI in hematology, with a focus on diagnostic automation, flow cytometry, genomic analysis, predictive modeling, and clinical decision support. A narrative literature review was conducted using PubMed, ScienceDirect, and Google Scholar, covering publications from 2018 to 2024. Keywords included: artificial intelligence, machine learning, deep learning, hematology, cytology, genomics, blood cancers. Articles presenting clinically validated or promising applications were selected. The findings indicate that AI is particularly applied in automated diagnostics and morphological analysis, especially in blood cell classification and the detection of morphological abnormalities. It also plays a pivotal role in flow cytometry and genomics, notably through the interpretation of next-generation sequencing (NGS) data to identify mutations and clinically relevant risk profiles, particularly in acute myeloid leukemia (AML). Furthermore, AI contributes to predictive modeling, patient follow-up, and morphometric quantitative image analysis, facilitating the early detection of hematologic malignancies. In conclusion, AI represents a major technological breakthrough in hematology, complementing human expertise while reducing workload. Future efforts should focus on enhancing algorithm explainability and clinical Regulatory harmonization and close collaboration between clinicians, data scientists, and institutions are essential to fully harness the potential of AI in the diagnosis, prognosis, and treatment of blood disorders.

There are three major types of blood cells, red blood cells (erythrocytes), white blood cells (leukocytes), and platelets (thrombocytes). Together, these three kinds of blood cells add up to a total of 45% of the blood tissue by volume, with the remaining 55% of the volume composed of plasma, the liquid component of blood. These three types play an important role in the human body by increasing immunity by fighting against infectious diseases. The classification and count of blood cells play an important role in the detection of a disease in an individual. It can also assist with the identification of diseases like infections, anemia, leukemia, cancer, etc. This classification will assist the hematologist to distinguish between the types of white blood cells, red blood cells, and platelets present in the human body and find the root cause of diseases. Currently, there is a large amount of research going on in this field. Considering a huge potential in the significance of the classification of different blood cells, a deep learning technique called Convolution Neural Networks will be used which can classify the images of human blood cells into their subtypes namely Neutrophils, Eosinophils, Basophils, Lymphocytes, Monocytes, Immature Granulocytes (Promyelocytes, Myelocytes, and Metamyelocytes), Red blood cells or Erythroblasts and Platelets or Thrombocytes. In this paper, the discussion have been done on a dataset that was acquired in the Core Laboratory at the Hospital Clinic of Barcelona using Convolution Neural Networks.

BackgroundHuntington’s disease is one of the rare neurodegenerative diseases caused because of genetic mutation of the Huntingtin gene. The major hallmarks of the condition include motor impairment, cognitive decline, and psychiatric symptoms. With no cure and only symptomatic treatments available, early detection and personalized therapy are warranted for managing the disease effectively. Artificial Intelligence has emerged as a transformational tool in healthcare, revolutionizing many parts of medical practice and research, thus holding the potential in detecting, monitoring, and managing Huntington’s disease.Main body of abstractArtificial Intelligence’s role in Huntington’s disease includes a variety of applications like medical image analysis and predictive analytics. AI-driven algorithms are utilized to analyze brain imaging data in medical image analysis. Deep learning and convolutional neural networks (CNNs) aid in the detection of subtle brain changes and the identification of illness biomarkers, allowing for the early diagnosis of the disease. Additionally, the predictive analytics capabilities of AI are used to analyze disease development and forecast clinical outcomes. AI models can identify illness patterns, estimate the rate of functional decline, and assist doctors in making educated decisions about treatment methods and care planning by analyzing patient data.ConclusionsWith clinical practice and research integrated with Artificial Intelligence technologies, we can significantly improve the quality of life of individuals affected with Huntington’s disease. This integration holds the potential to develop effective personalized interventions. Nevertheless, collaborative efforts among doctors, researchers, and technology sound developers would be key to the successful implementation of AI in HD.Graphical

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.

Background/Objectives: Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). Methods: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmentation techniques to improve model robustness and diversity. Extensive experiments were performed, including complete weight initialization, advanced optimization strategies, and meticulous hyperparameter tuning for the YOLOv11 architecture. Results: The YOLOv11-l model achieved an overall mean Average Precision (mAP) of 93.8%, reflecting its robust accuracy across multiple blood cell types. Conclusions: The findings underscore the efficacy of the YOLOv11 architecture in automating blood cell classification with high precision, demonstrating its potential to enhance hematological analyses and support clinical diagnosis.

Hematologic malignancy puts the lives of numerous people of all the ages at serious risk globally. Acute lymphoblastic leukemia (ALL), a sub type of leukemia, is a dreadful disease that affects millions of individual globally. Early detection is crucial, and image analysis of peripheral blood smears is a simple way to diagnose and categorize leukemic cells. However, image analysis is complex and can lead to incorrect findings. To address this, a model is proposed to examine ALL and identify its various categories using deep ensemble learning. This study shows that a deep learning method based on an ensemble weight threshold improves leukemia detection using imbalanced dataset which reflects the real-life scenario. The proposed model examines healthy and ALL cells and identify its various categories, including Benign, Pro-B, Early Pre-B, and Pre-B from peripheral blood smear images to help in the early detection of this life-threatening disease with the highest accuracy (99.96%), precision (99.86%), sensitivity (99.93%), specificity (99.97%), F1-Score (99.89%). The model also outperforms five other pre-trained networks, viz. AlexNet, VGG-16, DenseNet-210, ResNet-50, and MobileNetV2 in classification accuracy, demonstrating its potential as an effective early diagnostic tool for ALL and its subtypes

Leucocyte classification for leukaemia detection using image processing techniques.