Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring

An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder

Bone marrow (BM) is an essential part of the hematopoietic system, which generates all of the body's blood cells and maintains the body's overall health and immune system. The classification of bone marrow cells is pivotal in both clinical and research settings because many hematological diseases, such as leukemia, myelodysplastic syndromes, and anemias, are diagnosed based on specific abnormalities in the number, type, or morphology of bone marrow cells. There is a requirement for developing a robust deep-learning algorithm to diagnose bone marrow cells to keep a close check on them. This study proposes a framework for categorizing bone marrow cells into seven classes. In the proposed framework, five transfer learning models-DenseNet121, EfficientNetB5, ResNet50, Xception, and MobileNetV2-are implemented into the bone marrow dataset to classify them into seven classes. The best-performing DenseNet121 model was fine-tuned by adding one batch-normalization layer, one dropout layer, and two dense layers. The proposed fine-tuned DenseNet121 model was optimized using several optimizers, such as AdaGrad, AdaDelta, Adamax, RMSprop, and SGD, along with different batch sizes of 16, 32, 64, and 128. The fine-tuned DenseNet121 model was integrated with an attention mechanism to improve its performance by allowing the model to focus on the most relevant features or regions of the image, which can be particularly beneficial in medical imaging, where certain regions might have critical diagnostic information. The proposed fine-tuned and integrated DenseNet121 achieved the highest accuracy, with a training success rate of 99.97% and a testing success rate of 97.01%. The key hyperparameters, such as batch size, number of epochs, and different optimizers, were all considered for optimizing these pre-trained models to select the best model. This study will help in medical research to effectively classify the BM cells to prevent diseases like leukemia.

UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells

The differential count of white blood cells (WBCs) can effectively provide disease information for patients. Existing stained microscopic WBC classification usually requires complex sample-preparation steps, and is easily affected by external conditions such as illumination. In contrast, the inconspicuous nuclei of stain-free WBCs also bring great challenges to WBC classification. As such, image enhancement, as one of the preprocessing methods of image classification, is essential in improving the image qualities of stain-free WBCs. However, traditional or existing convolutional neural network (CNN)-based image enhancement techniques are typically designed as standalone modules aimed at improving the perceptual quality of humans, without considering their impact on advanced computer vision tasks of classification. Therefore, this work proposes a novel model, UR-Net, which consists of an image enhancement network framed by ResUNet with an attention mechanism and a ResNet classification network. The enhancement model is integrated into the classification model for joint training to improve the classification performance for stain-free WBCs. The experimental results demonstrate that compared to the models without image enhancement and previous enhancement and classification models, our proposed model achieved a best classification performance of 83.34% on our stain-free WBC dataset.

Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring

A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (<30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.

Machine learning-based approaches for identifying human blood cells harboring CRISPR-mediated fetal chromatin domain ablations

Two common hemoglobinopathies, sickle cell disease (SCD) and β-thalassemia, arise from genetic mutations within the β-globin gene. In this work, we identified a 500-bp motif (Fetal Chromatin Domain, FCD) upstream of human ϒ-globin locus and showed that the removal of this motif using CRISPR technology reactivates the expression of ϒ-globin. Next, we present two different cell morphology-based machine learning approaches that can be used identify human blood cells (KU-812) that harbor CRISPR-mediated FCD genetic modifications. Three candidate models from the first approach, which uses multilayer perceptron algorithm (MLP 20-26, MLP26-18, and MLP 30-26) and flow cytometry-derived cellular data, yielded 0.83 precision, 0.80 recall, 0.82 accuracy, and 0.90 area under the ROC (receiver operating characteristic) curve when predicting the edited cells. In comparison, the candidate model from the second approach, which uses deep learning (T2D5) and DIC microscopy-derived imaging data, performed with less accuracy (0.80) and ROC AUC (0.87). We envision that equivalent machine learning-based models can complement currently available genotyping protocols for specific genetic modifications which result in morphological changes in human cells.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Recent progress of inertial microfluidic-based cell separation

Cell separation has consistently been a pivotal technology of sample preparation in biomedical research. Compared with conventional bulky cell separation technologies applied in the clinic, cell separation based on microfluidics can accurately manipulate the displacement of liquid or cells at the microscale, which has great potential in point-of-care testing (POCT) applications due to small device size, low cost, low sample consumption, and high operating accuracy. Among various microfluidic cell separation technologies, inertial microfluidics has attracted great attention due to its simple structure and high throughput. In recent years, many researchers have explored the principles and applications of inertial microfluidics and developed different channel structures, including straight channels, curved channels, and multistage channels. However, the recently developed multistage channels have not been discussed and classified in detail compared with more widely discussed straight and curved channels. Therefore, in this review, a comprehensive and detailed review of recent progress in the multistage channel is presented. According to the channel structure, the inertial microfluidic separation technology is divided into (i) straight channel, (ii) curved channel, (iii) composite channel, and (iv) integrated device. The structural development of straight and curved channels is discussed in detail. And based on straight and curved channels, the multistage cell separation structures are reviewed, with a special focus on a variety of latest structures and related innovations of composite and integrated channels. Finally, the future prospects for the existing challenges in the development of inertial microfluidic cell separation technology are presented.

Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet

A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.

High-Efficiency Classification of White Blood Cells Based on Object Detection

White blood cells (WBCs) play a significant role in the human immune system, and the content of various subtypes of WBCs is usually maintained within a certain range in the human body, while deviant levels are important warning signs for diseases. Hence, the detection and classification of WBCs is an essential diagnostic technique. However, traditional WBC classification technologies based on image processing usually need to segment the collected target cell images from the background. This preprocessing operation not only increases the workload but also heavily affects the classification quality and efficiency. Therefore, we proposed one high-efficiency object detection technology that combines the segmentation and recognition of targets into one step to realize the detection and classification of WBCs in an image at the same time. Two state-of-the-art object detection models, Faster RCNN and Yolov4, were employed and comparatively studied to classify neutrophils, eosinophils, monocytes, and lymphocytes on a balanced and enhanced Blood Cell Count Dataset (BCCD). Our experimental results showed that the Faster RCNN and Yolov4 based deep transfer learning models achieved classification accuracy rates of 96.25% and 95.75%, respectively. For the one-stage model, Yolov4, while ensuring more than 95% accuracy, its detection speed could reach 60 FPS, which showed better performance compared with the two-stage model, Faster RCNN. The high-efficiency object detection network that does not require cell presegmentation can remove the difficulty of image preprocessing and greatly improve the efficiency of the entire classification task, which provides a potential solution for future real-time point-of-care diagnostic systems.