Blood Cell Images Research Articles

An analysis of decipherable red blood cell abnormality detection under federated environment leveraging XAI incorporated deep learning

In recent times, automated detection of diseases from pathological images leveraging Machine Learning (ML) models has become fairly common, where the ML models learn detecting the disease by identifying biomarkers from the images. However, such an approach requires the models to be trained on a vast amount of data, and healthcare organizations often tend to limit access due to privacy concerns. Consequently, collecting data for traditional centralized training becomes challenging. These privacy concerns can be handled by Federation Learning (FL), which builds an unbiased global model from local models trained with client data while maintaining the confidentiality of local data. Using FL, this study solves the problem of centralized data collection by detecting deformations in images of Red Blood Cells (RBC) in a decentralized way. To achieve this, RBC data is used to train multiple Deep Learning (DL) models, and among the various DL models, the most efficient one is considered to be used as the global model inside the FL framework. The FL framework works by copying the global model’s weight to the client’s local models and then training the local models in client-specific devices to average the weights of the local model back to the global model. In the averaging process, direct averaging is performed and alongside, weighted averaging is also done to weigh the individual local model’s contribution according to their performance, keeping the FL framework immune to the effects of bad clients and attacks. In the process, the data of the client remains confidential during training, while the global model learns necessary information. The results of the experiments indicate that there is no significant difference in the performance of the FL method and the best-performing DL model, as the best-performing DL model reaches an accuracy of 96% and the FL environment reaches 94%-95%. This study shows that the FL technique, in comparison to the classic DL methodology, can accomplish confidentiality secured RBC deformation classification from RBC images without substantially diminishing the accuracy of the categorization. Finally, the overall validity of the classification results has been verified by employing GradCam driven Explainable AI techniques.

Utility of digital cell imaging as an aid to enhance pathologist peripheral blood smear review for routine and on call practice: A verification study

Abstract Introduction/Objective Microscopic examination of peripheral blood smear (PBS) provides important information to support the diagnosis of hematological diseases. However, manual microscopic examination of PBS is labor-intensive and cannot be done remotely. CellaVision is digital cell imaging technology that provides pre-classification and digital imaging of the red blood cells (RBCs), white blood cells (WBCs), and platelets, myeloid, lymphoid and erythroid precursors. Several studies have evaluated the performance of this technology in the hematology laboratory as tool to assist laboratory technologists; our study evaluates its use for pathologist review of peripheral blood smears both during routine and on call practice. Methods/Case Report Our study included 30 PBS and two hematopathologists with greater than 10 years’ experience in performing PBS review. We compared pathologist’s own impression of microscopic PBS review to their own impression using CellaVision software (intraobserver comparison). Additionally, pathologist to pathologist impression was compared for both methods (interobserver comparison). All 30 cases revealed abnormal findings including, but not limited to: pancytopenia, leukocytosis, leukopenia, anemia, thrombocytopenia, and thrombocytosis. Of the 30 cases, 5 “on-call” were cases when the pathologist had to come on–site for pathologist PBS review. Results (if a Case Study enter NA) The pathologists own PBS interpretation (intraobserver) by microscopy and CellaVision had a concordance of 96.7%. The pathologist to pathologist interpretation of both methods had a concordance of 96.7%.The one discrepant case was a smear with platelet clumping and normal platelet count. On chart review of this case, this discrepancy would have minimal impact on patient care. Conclusion Pathologist use of CellaVision in their routine practice may result in speedier PBS review, particularly in cases with leukopenia, and better communication with laboratory technologists. CellaVision is less reliable in identifying platelet clumps and should not be used in isolation in evaluating thrombocytopenia. Used with other relevant data, CellaVision has the potential to preempt in person microscopic review of PBS when on-call in certain situations.

A large multi-focus dataset for white blood cell classification

The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital microscopy has emerged as a solution to reduce this labor-intensive process and improve efficiency. Several publicly available datasets provide various WBC subtypes of differing quality and resolution. These datasets have contributed to advancing WBC classification using machine learning techniques. However, digital microscopy of blood cells with high magnification often requires a wider depth of field, posing challenges for automatic digital microscopy that necessitates capturing multiple stacks of focal planes to obtain complete images of specific blood cells. Our dataset provides 25,773 image stacks from 72 patients. The image labels consist of 18 classes encompassing normal and abnormal cells, with two experts reviewing each label. Each image includes 10 z-stacks of cropped 200 by 200 pixel images, captured using a 50X microscope with 400 nm intervals. This study presents a comprehensive multi-focus dataset for WBC classification.

Comparing Human-Level and Machine Learning ModelPerformance in White Blood Cell MorphologyAssessment.

There is an increasing research focus on the role of machine learning in the haematology laboratory, particularly in blood cell morphologic assessment. Human-level performance is an important baseline and goal for machine learning. This study aims to assess the interobserver variability and human-level performance in blood cell morphologic assessment. A dataset of 1000 single white blood cell images were independently labelled by 10 doctors and morphology scientists. Interobserver variability was calculated using Fleiss' kappa. Observers' labels were then separated into consensus labels used to determine ground truth, and performance labels used to assess observer performance. A machine learning model was trained and assessed using the same cell images. Explainability images (XRAI and IG) were generated for each of the test images. The Fleiss kappa for all 10 observers was 0.608, indicating substantial agreement between observers. The accuracy of human observers was 95%, with sensitivity 72% and specificity 97%. The accuracy of the machine learning model was 95%, with sensitivity 71% and specificity 97%. The model shared similar performance across labels when compared to humans. Explainability metrics demonstrated that the machine learning model was able to differentiate between the cytoplasm and nucleus of the cells, and used these features to perform predictions. The substantial, though not perfect, agreement between human observers highlights the inherent subjectivity in white blood cell morphologic assessment. A machine learning model performed similarly to human observers in single white blood cell identification. Further research is needed to compare human-level and machine learning performance in ways that more closely reflect the typical process of morphologic assessment.

A fine-grained image classification algorithm based on self-supervised learning and multi-feature fusion of blood cells

Leukemia is a prevalent and widespread blood disease, and its early diagnosis is crucial for effective patient treatment. Diagnosing leukemia types heavily relies on pathologists’ morphological examination of blood cell images. However, this process is tedious and time-consuming, and the diagnostic results are subjective, leading to potential misdiagnosis and underdiagnosis. This paper proposes a blood cell image classification method that combines MAE with an enhanced Vision Transformer to tackle these challenges. Initially, pre-training occurs on two datasets, TMAMD and Red4, using the MAE self-supervised learning algorithm. Subsequently, the pre-training weights are transferred to our improved model.This paper introduces feature fusion of the outputs from each layer of the Transformer encoder to maximize the utilization of features extracted from lower layers, such as color, contour, and texture of blood cells, along with deeper semantic features. Furthermore, the dynamic margins for the subcenter Arcface Loss function are employed to enhance the model’s fine-grained feature representation by achieving inter-class dispersion and intra-class aggregation. Models trained using our method achieved state-of-the-art results on both the TMAMD dataset and Red4 dataset, with classification accuracies of 93.51% and 81.41%, respectively. This achievement is expected to be a valuable reference for physicians in their clinical diagnoses.

Compressed sensing reconstruction for high-SNR, rapid dissolved 129Xe gas exchange MRI.

Three-dimensional hyperpolarized 129Xe gas exchange imaging suffers from low SNR and long breath-holds, which could be improved using compressed sensing (CS). The purpose of this work was to assess whether gas exchange ratio maps are quantitatively preserved in CS-accelerated dissolved-phase 129Xe imaging and to investigate the feasibility of CS-dissolved 129Xe imaging with reduced-cost natural abundance (NA) xenon. 129Xe gas exchange imaging was performed at 1.5 T with a multi-echo spectroscopic imaging sequence. A CS reconstruction with an acceleration factor of 2 was compared retrospectively with conventional gridding reconstruction in a cohort of 16 healthy volunteers, 5 chronic obstructive pulmonary disease patients, and 23 patients who were hospitalized following COVID-19 infection. Metrics of comparison included normalized mean absolute error, mean gas exchange ratio, and red blood cell (RBC) image SNR. Dissolved 129Xe CS imaging with NA xenon was assessed in 4 healthy volunteers. CS reconstruction enabled acquisition time to be halved, and it reduced background noise. Median RBC SNR increased from 6 (2-18) to 11 (2-100) with CS, and there was strong agreement between CS and gridding mean ratio map values (R2 = 0.99). Image fidelity was maintained for gridding RBC SNR > 5, but below this, normalized mean absolute error increased nonlinearly with decreasing SNR. CS increased the mean SNR of NA 129Xe images 3-fold. CS reconstruction of dissolved 129Xe imaging improved image quality with decreased scan time, while preserving key gas exchange metrics. This will benefit patients with breathlessness and/or low gas transfer and shows promise for NA-dissolved 129Xe imaging.

TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.

As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in blood cell detection. To address blood cell detection, we developed the TW-YOLO approach, leveraging multi-scale feature fusion techniques. Firstly, traditional CNN (Convolutional Neural Network) convolution has poor recognition capabilities for certain blood cell features, so the RFAConv (Receptive Field Attention Convolution) module was incorporated into the backbone of the model to enhance its capacity to extract geometric characteristics from blood cells. At the same time, utilizing the feature pyramid architecture of YOLO (You Only Look Once), we enhanced the fusion of features at different scales by incorporating the CBAM (Convolutional Block Attention Module) in the detection head and the EMA (Efficient Multi-Scale Attention) module in the neck, thereby improving the recognition ability of blood cells. Additionally, to meet the specific needs of blood cell detection, we designed the PGI-Ghost (Programmable Gradient Information-Ghost) strategy to finely describe the gradient flow throughout the process of extracting features, further improving the model's effectiveness. Experiments on blood cell detection datasets such as BloodCell-Detection-Dataset (BCD) reveal that TW-YOLO outperforms other models by 2%, demonstrating excellent performance in the task of blood cell detection. In addition to advancing blood cell image analysis research, this work offers strong technical support for future automated medical diagnostics.

A deep learning approach for white blood cells image generation and classification using SRGAN and VGG19

The classification of White Blood Cells (WBCs) is crucial for diagnosing diseases, monitoring treatment effectiveness, and understanding how the immune system functions. In this paper, we propose a deep learning approach to classify WBCs using Super Resolution Generative Adversarial Network (SRGAN) and Visual Geometry Group 19 (VGG19). Firstly, microscopic images of WBCs are generated using the SRGAN to obtain more precise and high-resolution images, which are then classified with a pretrained VGG19 classifier. Low-resolution (LR) images are inputted into the generator of SRGAN, and its discriminator compares the High-resolution (HR) image with LR, generating super-resolution images to minimize misclassification risks. A large dataset of 12,447 images containing four classes of WBCs (Eosinophil, Lymphocyte, Monocyte, and Neutrophil) is utilized to train and validate our proposed model. Following extensive experimental analysis, our proposed model achieves a test accuracy of 94.87 %, surpassing traditional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Hybrid CNN-RNN models, and other conventional approaches. The generated images of SRGAN overcome challenges associated with misclassification due to the poor resolution of microscopic images, while the use of a pretrained model as a classifier reduces classification complexity. The source code of the entire work is available at https://github.com/Jannatul-Ferdousi/SRGAN_VGG19_WBC.git.

Leukocyte classification using relative-relationship-guided contrastive learning

Hematologic diseases and blood disorders can be studied through microscopic examination of blood smear images or chemical assays. Many researchers are focused on utilizing deep learning (DL) to identify, quantify, and classify various types of blood cells, which is crucial for addressing theoretical and practical challenges in disease diagnosis and treatment planning. However, there is a significant deficiency in annotations for leukocyte classification, limiting the effectiveness of current DL methods. Contrastive learning (CL) facilitates the extraction of prior knowledge from unlabeled data, reducing the dependency on manual annotations. While CL demonstrates remarkable achievements in classifying natural images, the direct application to leukocyte classification presents certain defects. We observe that standard data augmentation techniques in CL exhibit varying sensitivity to different categories of white blood cell images. Employing a uniform augmentation strategy may lead the network into an optimization trap, acquiring inadequate representations from erroneous sample relationships. To address this issue, we propose a Relative-Relationship-Guided Contrastive Learning Representation (ReCLR) framework for the leukocyte classification. ReCLR mines positive and negative samples based on relative distance knowledge. Specifically, we provide adversarial guidance for the positive sample, restricting the distance between the positive sample and the original sample less than but as close as possible to the distance between the original sample and the farthest negative sample. Then, we utilize the entropy constraint to regulate the relative distance relationship between the negative and original samples. Finally, the guided positive and negative samples are employed for contrastive learning in leukocyte classification. The extensive experiments, including linear evaluation, domain transfer, and fine-tuning, demonstrate the effectiveness of the proposed method. Our ReCLR achieves accuracies of 92.07%, 65.36%, and 92.49% on three real-world leukocyte datasets, respectively, outperforming several state-of-the-art methods. The source code is released at https://github.com/AlchemyEmperor/ReCLR.

Optimized automated blood cells analysis using Enhanced Greywolf Optimization with integrated attention mechanism and YOLOv5

Blood testing is widely regarded as a fundamental diagnostic procedure in healthcare, with blood cell counting being particularly vital for diagnosing conditions and evaluating overall health. Traditional methods employing hemocytometers and diverse laboratory instruments are typically employed to conduct blood counts manually. Using a microscope, manually counting, and examining blood cells takes time and effort. Thus, developing autonomous blood cells detecting and counting cells in this system becomes necessary to help doctors diagnose patients quickly and accurately. The proposed method integrates the YOLOv5 object detection framework with Enhanced Graywolf Optimization (EGWO) and an Attention Mechanism to improve the accuracy and efficiency of RBC detection. The YOLOv5 architecture is optimized for the task through the EGWO algorithm, which fine-tunes the model parameters, while the Attention Mechanism focuses on extracting critical features from blood cell images. The model is trained and validated using the BCCD dataset, employing an 80/20 split for training and validation. Data augmentation techniques, such as random rotations, flips, and color adjustments, are applied to enhance model generalization. Experimental results demonstrate that our method achieves a high accuracy of 99.89 %, outperforming existing models like Faster R-CNN, CNN, and DCNN regarding accuracy, training time, and inference speed. The proposed method's moderate complexity and fast inference time make it suitable for real-time applications in clinical settings. This research provides a robust and efficient solution for automated RBC detection and counting, with potential implications for improving diagnostic processes in hematological analyses. Overall, in practical applications, it is helpful for counting red blood cells from smeared pictures in less than a second.

Evaluation of blood evacuation efficiency using 99m Tc-RBC imaging: a comparative study of exsanguination tourniquet rings and Esmarch bandages.

The objective of this study is to compare the effectiveness of the Esmarch bandage and exsanguination tourniquet rings (ETRs) in blood evacuation procedures using a controlled intra-subject design involving healthy volunteers. A total of 20 healthy adult volunteers (12 males, 8 females) were recruited from the community. Participants underwent blood evacuation procedures on both legs, using the Esmarch bandage on one leg and the ETR on the other. The order of the procedures was randomized. Blood evacuation time, overall blood evacuation rate, and calf blood evacuation rate were measured using 99m Tc-labeled red blood cell imaging. Paired t -tests were conducted to compare the effectiveness of the two methods. The ETRs demonstrated a significantly faster blood evacuation time compared to the Esmarch bandage (mean difference = -41.72 s, P < 0.0001). The overall blood evacuation rate was slightly higher for the ETRs (mean difference = 1.717%), though not statistically significant ( P = 0.3680). The calf blood evacuation rate was significantly higher for the ETRs (mean difference = 6.86%, P = 0.0225). No significant discomfort or adverse reactions were reported by any participants. ETRs are more efficient in terms of blood evacuation time and calf blood evacuation rate compared to the Esmarch bandage, without causing significant discomfort or adverse reactions. These findings suggest that ETRs could be a preferable option in clinical settings for blood evacuation procedures.

Eliminating the systematic error of spatial light interference microscopy by the wavefront comparison iterative algorithm

Spatial light interference microscopy (SLIM) is a unique quantitative phase imaging technology. Since the phase of direct light is simply assumed to be 0, it has a non-negligible systematic error. Based on the theory of partially coherent SLIM, here we propose a wavefront comparison iterative (WCI) algorithm to eliminate this error for accurate phase imaging. In this paper, we clarified the existence of this systematic error by phase imaging of oil-immersed polystyrene microspheres, and we demonstrated the feasibility of the WCI algorithm by phase imaging of red blood cells. Furthermore, through computer simulation, we have confirmed that for phase imaging of large adherent cells, if the SLIM system compresses the phase of direct light sufficiently small, the WCI algorithm will be feasible too.

Revolutionizing malaria diagnosis: deep learning-powered detection of parasite-infected red blood cells

Malaria is a significant global health issue, responsible for the highest rates of morbidity and mortality globally. This paper introduces a very effective and precise convolutional neural network (CNN) method that employs advanced deep learning techniques to automate the detection of malaria in images of red blood cells (RBC). Furthermore, we present an emerging and efficient deep learning method for differentiating between cells infected with malaria and those that are not infected. To thoroughly evaluate the efficiency of our approach, we do a meticulous assessment that involves comparing different deep learning models, such as ResNet-50, MobileNet-v2, and Inception-v3, within the domain of malaria detection. Additionally, we conduct a thorough comparison of our proposed approach with current automated methods for malaria identification. An examination of the most current techniques reveals differences in performance metrics, such as accuracy, specificity, sensitivity, and F1 score, for diagnosing malaria. Moreover, compared to existing models for malaria detection, our method is the most successful, achieving an accurate score of 1.00 in all statistical matrices, confirming its promise as a highly efficient tool for automating malaria detection.

An attention-based deep learning for acute lymphoblastic leukemia classification

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks’ feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.

Gold-antibody-aptamer complexed electrochemical sensing surface for septic arthritis biomarker determination

Septic arthritis (SA) is more severe in patients with rheumatoid arthritis, joint surgical issues, or a weakened immune system. Timely diagnosis of SA is crucial for effective treatment. Traditional diagnostic methods such as ELISA, white blood cell counting, blood culture, qPCR, and imaging techniques are often less accurate and time-consuming. Researchers are focusing on developing highly sensitive biosensors for SA using blood-based biomarkers. Procalcitonin is a protein and a well-established biomarker for SA. This research focuses on developing a procalcitonin interdigitated electrode (IDE) biosensor using a probe made of an aptamer and antibody-modified gold nanoparticle (AuNP) complex. The probe was attached to the IDE through an amine linker and then interacted with procalcitonin. AuNPs increased the attachment of the aptamer and antibody to the IDE, enabling the detection of procalcitonin at levels as low as 10 ng/mL, with a linear regression curve ranging from 10 to 100 ng/mL [y = 4.0691x - 2.1887; R2 = 0.9937]. Furthermore, procalcitonin-spiked serum elevated the current level with increasing procalcitonin concentrations, while control performances did not enhance the current, indicating the selective and specific detection of procalcitonin. This AuNP-aptamer-antibody complexed biosensor effectively identifies procalcitonin at low levels and aids in the diagnosis of SA.

Classification of rotation-invariant biomedical images using equivariant neural networks

Transmission electron microscopy (TEM) is an imaging technique used to visualize and analyze nano-sized structures and objects such as virus particles. Light microscopy can be used to diagnose diseases or characterize e.g. blood cells. Since samples under microscopes exhibit certain symmetries, such as global rotation invariance, equivariant neural networks are presumed to be useful. In this study, a baseline convolutional neural network is constructed in the form of the commonly used VGG16 classifier. Thereafter, it is modified to be equivariant to the p4 symmetry group of rotations of multiples of 90° using group convolutions. This yields a number of benefits on a TEM virus dataset, including higher top validation set accuracy by on average 7.6% and faster convergence during training by on average 23.1% of that of the baseline. Similarly, when training and testing on images of blood cells, the convergence time for the equivariant neural network is 7.9% of that of the baseline. From this it is concluded that augmentation strategies for rotation can be skipped. Furthermore, when modelling the accuracy versus amount of TEM virus training data with a power law, the equivariant network has a slope of − 0.43 compared to − 0.26 of the baseline. Thus the equivariant network learns faster than the baseline when more training data is added. This study extends previous research on equivariant neural networks applied to images which exhibit symmetries to isometric transformations.

Hematological cytomorphology: Where we are.

The manuscript discusses the historical evolution of observing blood cell morphology under an optical microscope, from the earliest microscopes in the 17th century to the modern digital era, highlighting key advancements and contributions in the field. Blood has historically held symbolic importance in various cultures, with early medical observations dating back to Hippocrates and Galeno. The discovery of cells and subsequent advancements in microscopy by scientists like Hooke and van Leeuwenhoek paved the way for understanding blood cell morphology. Influential figures such as Hewson, Donné, and Ehrlich followed. Diagnostic cytology evolved from manual cell counting to the development of automated hematological systems. Automated complete blood counting came to support microscopic examination in diagnosing hematological disorders. Morphology is crucial in predicting disease outcomes and guiding treatment decisions, particularly hematological neoplasms. The introduction of flow cytometry and its integration with traditional morphological analysis and the new cytogenetic and molecular techniques revolutionized the classification and prognostication of hematologic disorders. Digital microscopy has emerged as a powerful tool in recent years, offering rapid acquisition and sharing of blood cell images. Integrating Artificial Intelligence with digital microscopy has further enhanced morphological analysis, improving diagnostic efficiency. We also discuss the prospects of AI in pre-classifying blood cells in bone marrow aspirate samples, potentially revolutionizing diagnostic pathways for hematologic diseases. Overall, the manuscript provides a comprehensive overview of the historical development, clinical significance and technological advancements in observing blood cell morphology, underscoring its continued relevance in modern hematology practice.

Efficient deep learning-based approach for malaria detection using red blood cell smears

Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.

Optofluidic time-stretch imaging flow cytometry with a real-time storage rate beyond 5.9 GB/s.

Optofluidic time-stretch imaging flow cytometry (OTS-IFC) provides a suitable solution for high-precision cell analysis and high-sensitivity detection of rare cells due to its high-throughput and continuous image acquisition. However, transferring and storing continuous big data streams remains a challenge. In this study, we designed a high-speed streaming storage strategy to store OTS-IFC data in real-time, overcoming the imbalance between the fast generation speed in the data acquisition and processing subsystem and the comparatively slower storage speed in the transmission and storage subsystem. This strategy, utilizing an asynchronous buffer structure built on the producer-consumer model, optimizes memory usage for enhanced data throughput and stability. We evaluated the storage performance of the high-speed streaming storage strategy in ultra-large-scale blood cell imaging on a common commercial device. The experimental results show that it can provide a continuous data throughput of up to 5891 MB/s.

An Efficient Algorithm for Acute Lymphoblastic Leukemia Quantification in Blood Cell Images Using Support Vector Machines Algorithm

As the capabilities of information technologies develop, the effectiveness of using the capabilities of CNN convolutional neural networks, an algorithm with a high level of accuracy, and early prediction of various diseases in blood cell images is high. This study proposes an effective method for early detection of acute lymphoblastic leukemia in blood cell images using Support Vector Machines (SVM) algorithm. In this method, a pattern of cells is recognized and used to identify cell markers specific to leukemia. This algorithm is used to match leukemia to a single marker in the cell image. Comparing SNN convolutional neural network algorithms with random forest (RF), Bayesian classifier, Support Vector Machines (SVM) and K nearest neighbor (KNN) algorithms, the results obtained by Support Vector Machines (SVM) were found to be 90.9% efficient.