Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network

Jain, Nirav; Shah, S. S. H.; Mangrulkar, Ramchandra; Sonawane, Pankaj

doi:10.1201/9781003133681-5

Cited by 4 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To be resolved, a machine learning model can forecast when the data input and the desired result are known, but not the means to get there. We know the parameters and method for obtaining the result in traditional programming methods, but with machine learning, the machine learns and presents the approach (ETHEM, 2016) [ 7 ]. Every machine learning model goes through data preparation, training, and testing.…”

Section: Theoretical Approachmentioning

confidence: 99%

Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning

Alhazmi

2022

BioMed Research International

View full text Add to dashboard Cite

A blood count is one of the most important diagnostic tools in medicine and one of the most common procedures. It can reveal important changes in the body and is commonly used as the first stage in the process of evaluating patients’ health. Even though this is a common practice, delivering examinations in laboratories can be difficult due to the availability of expensive technology that requires frequent maintenance. This study is developing an alternative deep learning computational model capable of automatically detecting cells in images of blood samples. Using object detection libraries, it was possible to train a model that was focused on this task and capable of detecting cells in images with adequate accuracy. When the identification of cells in images of blood samples was taken into account in the best results obtained, it was possible to count white cells with an accuracy of one hundred percent, red cells with an accuracy of 89%, and platelets with an accuracy of 96%, which generated subsidies to develop the primary components of a blood count. The components that were supposed to classify the various types of white cells were not carried out due to the limits of the dataset provided. On the other hand, the study can be broadened to include further works that deal with this issue because it produced satisfactory results.

show abstract

Section: Theoretical Approachmentioning

confidence: 99%

Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning

Alhazmi

2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…In other words, the collaboration of two trackers contributes to a more robust monitoring scheme because of the conceptual difference and opposing tracking orientations. Red blood cells flow in fixed orientations along capillaries in OBC movies, which roughly fits the SORT concept of predictable velocities Jain et al, [13]. CycleTrack combines SORT and CenterTrack into a robust tracking system that tracks and counts erythrocytes.…”

Section: Simple Online and Real-time Tracking [Sort]mentioning

confidence: 52%

Performing Red Blood Cell Count Using Video Capillaroscopy and Analyzing the Data Using MATLAB Software

Fesharakinia

2024

Preprint

View full text Add to dashboard Cite

<p>Complete blood cell count (CBC) is one of the most common blood tests. The current CBC is performed invasively through puncture of the skin drawing blood sample, which can put cancer patients and immunocompromised patients at risk of infection and hospitalization. Also, people with Hemophilia might suffer from bleeding after capillary puncture for up to 4 hours. By performing capillaroscopy, (a non-invasive technique to visualize capillaries) and by processing and analyzing the data via software such as MATLAB, we hypothesized that we would be able to provide Red Blood Cell (RBC) counting without skin puncture thus acquiring valuable information about one's health non-invasively. The size of a blood capillary is comparable with a RBC size, which means that we can observe RBC in the capillaries cell by cell. Focusing on nailfold capillaries, we can monitor single capillaries across a field of view (FOV). Separating and quantifying the light absorption gaps in the nail capillaries in vivo is an effective tool for counting the number of red blood cells in the body and diagnosing disorders caused by a deficiency or increase of RBCs. In this project, we have processed video footage of nail fold capillaries via MATLAB and were able to detect and count red blood cells in-vivo.</p>

show abstract

“…As can be observed, the works conducted by [9,[12][13] were monitored to produce a higher percentage of accuracy than the proposed approach. It should be emphasized, however, that the results compared were obtained using different datasets, as well as a smaller number of image datasets.…”

Section: Resultsmentioning

confidence: 92%

“…Accordingly, the outcomes of the proposed approach were compared to those produced by other researchers working on platelet counting as in [8][9][11][12], using the most often used values of accuracy, sensitivity, and specificity for counting purposes. The selected methods work on the basis of traditional image processing and Deep Learning.…”

Section: Resultsmentioning

confidence: 99%

“…A vast number of independent experiments in a variety of modalities and applications, such as those for autonomous cell counting, have demonstrated the efficiency and utility of Deep Learning in the medical imaging area. Nirav et al [9] implemented the Deep Learning approach which effectively and rapidly processes the images for the count of WBCs, RBCs, and platelet.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automated Platelet Counter with Detection Using K-Means Clustering

Ibrahim,

Fauzi,

Mangshor

et al. 2023

AETiC

View full text Add to dashboard Cite

Platelet is a blood cell type that is stored and circulated in the human body. It acts as a blood thickening agent and prevents blood from overflowing whenever bleeding occurs. An excessive or inadequate number of platelets could lead to platelet-related diseases. The current practice of platelet counting involves the manual counting process using a haemocytometer, Wright’s Stain which uses the dyes to facilitate the differentiation of blood cell types, and a tally counter. Yet, this process can be time-consuming, demanding, and exhausting for haematologists, and likely to be prone to errors. Thus, this paper presents a study on automated platelet counter and detection using image processing techniques. The K-Means Clustering was employed to count and detect the presence of platelets in microscopic blood smear images. Several processes were performed prior to the K-means clustering, including image enhancement and YCbCr image formatting. Subsequently, image masking, as well as area thresholding were applied to eliminate every unwanted entity and highlight the visibility of the platelets before the number of platelets could be detected and counted. A comparative experiment was designed in which the K-Means Clustering platelet count and detection were compared with the actual number of platelets reported by haematologists. The platelet counts and detection were categorized into three detection categories which are Less Detection (LD), Accurate Detection (AD), and Over Detection (OD). The proposed study was evaluated to 90 testing platelet images. Out of the 90 testing images, 75 platelet images were perfectly counted and detected which returned 91.67% of accuracy. This signifies that the K-Means Clustering algorithm was discovered to be efficient and dependable for automated platelet counter and detection

show abstract

Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network

Cited by 4 publications

References 0 publications

Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning

Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning

Performing Red Blood Cell Count Using Video Capillaroscopy and Analyzing the Data Using MATLAB Software

Automated Platelet Counter with Detection Using K-Means Clustering

Contact Info

Product

Resources

About