Blood cell classification using interpretable shape features: A Comparative study of SVM models and CNN-Based approaches

Leucocyte classification for leukaemia detection using image processing techniques.

SummaryManual investigation of blood cell count is sometimes erroneous due to interoperability error, fatigue error, requiring expert skill and time consuming too. In particular, investigation of white blood cell (WBC) gains importance in identifying diseases like leukemia, leukopenia, etc. WBC does not possess regular structure because they move throughout the blood stream and hence analyzing WBC and its types for structure and shape is quite challenging. To aid in hematology, this work provides classification of WBC classification based on modified U‐Net and support vector machines (SVM). A modified U‐Net architecture is developed to segment WBC followed by feature extraction and classification by radial basis function‐support vector machine (RBF‐SVM). Experiments indicated that the modified U Net segmentation can detect the WBC nucleus with a dice similarity coefficient of 0.972. The proposed U‐Net‐SVM can recognize WBCs in Raabin‐WBC, LISC, and BCCD datasets with an accuracy of 99.45%, 98.62%, and 98.81%, respectively. Further investigation on leukemia dataset, ALL‐IDB2, revealed an accuracy of 99.42% with 100% sensitivity and specificity. The proposed model can be used to investigate WBCs and hence provide a great support to the hematologists in analyzing the blood smear for various disease identifications.

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.

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.

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.

Among the blood cells, white blood cells (WBC), which play a crucial role in forming our body's defense system, are essential components. Originating in the bone marrow, these cells serve as the fundamental components of the immune system, shouldering the responsibility of safeguarding the body against foreign microbes and diseases. Insufficient WBC counts may compromise the body's skill to resist infections, a status known as leukopenia. White blood cell counting is a specialty procedure that is usually carried out by qualified physicians and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network (CNN) models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed CNN model.

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.

Blood cell classification is a recent topic for scientists working on the diagnosis of blood cell related illnesses. As the number of computer vision (CV) applications is increasing to improve quality of human life, it spreads in the areas of autonomous drive, surveillance, robotic applications, telecommunications and etc. The number of CV applications also increases in the medical sector due the decreasing value of doctors per patient ratio (DPPR) in urban and suburban areas. A doctor working in such areas sometimes would have to interpret thousands of patients’ test results in a day. This condition would result disadvantages such as false diagnosis on patients and break on working motivations for doctors. Some of the tests would probably be interpreted using an application developed by Artificial Neural Networks (ANN). Tests related to blood cells are examined for the patients as a starting point of diagnosis and information obtained about their abnormalities give doctors a preliminary idea about the illnesses. This article issues generation of a CV application that would be used as an assistant of doctors who have domain expertise. The article issues segmentation of blood cells, classification of red and white blood cells containing 6 types such as erythrocyte, lymphocyte, platelets, neutrophil, monocytes and eosinophils using the segmentation results. It also discusses about a method for detection of abnormalities on red blood cells (erythrocyte).

Automated classification of white blood cells from microscope images is still challenging, particularly in terms of feature representations choice considering its complexity, compactness and efficiency. Particularly, in this scenario, the feature representations have to be invariant to non-uniform illumination, shape of the nuclei, stage of maturity, change in topology due to rotation, scale and shifting. This paper proposes a new white blood cell feature representation which aims at increasing robustness to those challenges. The proposed feature representation is designed based on L-moments (L-skewness, L-mean, L-scale and L-kurtosis) of the Radon projection of segmented nuclei shape. Coupled with Linear Discriminant Analysis, the proposed feature representation has been shown to be highly effective at encoding the discriminative properties of the white blood cells, and invariant to intra-class cell variations. Support Vector Machine (SVM) based (ones-vs-all) schema and a classification tree are applied to separate the multiple classes of cells. The proposed approach is evaluated for a 10-class problem. It achieves an average classification accuracy of 97.23% and outperforms all other feature representations, including bispectral invariant, local binary pattern, and histogram of oriented gradients using the same classifier on the same dataset. The proposed method is also compared and benchmarked with the other 12 existing techniques for classification of white blood cells into 10 classes over the same datasets and the results show that the proposed method achieves high accuracy in comparison with other approaches. The proposed method is also highly competitive in terms of computation and efficiency in comparison with other approaches.

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.

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.

Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

Our heart pumps red blood constantly, carrying waste products, hormones, nutrition, oxygen, and other essential health markers. Blood cells in this fluid are made up of many components, including platelets, RBCs, and WBCs. Neutrophils, Lymphocytes, Monocytes, Eosinophils, and Basophils are further subdivided into WBCs. To precisely identify and treat numerous kinds of blood-related illnesses, the detection and analysis of these blood cells is necessary for advanced diagnostics in medicine. Convolutional neural networks (CNNs), for example, are highly relied upon in modern medical diagnostics for the examination of blood cells. The precision and potency of blood cell identification procedures have been greatly improved by CNNs and Additional approaches to deep learning like RNNs and YOLO. The persistence of this research is to list challenges in White Blood cell detection and classification problem. Deep learning-based blood cell identification and classification approaches based on Single Stage approaches. Single Stage Approach involve You only look Once (YOLO). It explores the various architectures for feature selection and classification starting from YoloV1 toV5 and it’s various modifications. In this paper would be looking at things like network parameters, training procedure, and transfer learning application for each variation. It also assesses model complexity, accuracy, performance, generalization, computational efficiency, and transfer learning capabilities, in addition to variables like data amount and source.

Proper classification of white blood cells (WBCs) remains a critical task for medical diagnosis in leukemia, infections, and hematological disorders. WBC classification is based mainly on Convolutional Neural Networks (CNNs) as the main methodology, despite the challenges in such methods in scanning microscopic images for distant dependencies. The current study adopts a hybrid model that combines Vision Transformers (ViTs) with CNNs for better WBC classification. The model has a ViT component that uses self-attention mechanisms to obtain whole-image features through global features extraction, while at the same time, neighborhood information is being extracted by the CNN module. The introduced model is evaluated using theBCCD Dataset (Blood Cell Count and Detection) for binary and multi-class separation of granulocytes/agranulocytes. Experimental tests indicate that the combined approach has robust classification accuracy with guaranteed reliable results in various evaluation metrics. The suggested method achieved 99.20% training accuracy, 87.90% test accuracy, a precision of 0.7083, a recall of 0.7000, and an F1-score of 0.6970, affirming its excellent classification precision. This hybrid deep learning model shows enhanced performance and interpretability, supporting its application in clinical diagnostic processes. These findings attest to excellent classification performance, positioning the model as an excellent choice for automated hematological diagnosis. The study opens up new horizons for combining CNNs and ViTs for improved medical image analysis while setting future development goals in WBC classification. Deep learning hybrid models have shown their significance for clinical diagnostics in the study through the development of enhanced detection systems against hematological diseases.

The common method of red and white blood cells identification and counting consider the manual processes on microscope which is arranged by the laboratory’s technician with their own experience. In this research, we will develop a computer program to detect and identify the proposed objects based on their pattern. Our proposed method is very effective in cell detection. The proposed objects are Red Blood Cells (RBCs), and White Blood Cells (WBCs). For blood cells identification and classification, an idea of Viola and Jones will be followed. Adaboost (adaptive boosting) method will be applied to increase the accuracy of the error of learning algorithm. The output of the proposed program shows that all the types of cells mentioned can be detected and classify effectively by showing the number and time spent of cells detected.