scholarly journals Blood Group Detection using Matlab

2021 ◽  
Vol 9 (VI) ◽  
pp. 3089-3093
Author(s):  
Sujatha C. N
Keyword(s):  
Image Processing ◽  
Blood Group ◽  
Human Error ◽  
Group Identification ◽  
Group Testing ◽  
Emergency Situation ◽  
Blood Type ◽  
Morphological Operations ◽  
Laboratory Staff ◽  
Emergency Situations

Blood group testing is one of the vital tasks in the area of medicine, in which it is very important during emergency situation before victim requires blood transfusion. Presently, the blood tests are conducted manually by laboratory staff members, which is time consuming process in the emergency situations. Blood group identification within shortest possible time without any human error is an important factor and very much essential. Image processing paves a way in determining blood type without human intervention. Images which are captured using high resolution microscopic camera during the blood slide test in the laboratory which are used for blood type evaluation. The image processing techniques which include thresholding and morphological operations are used. The blood image is separated into sample wise and blood type is decided based on the agglutination effects in those sample images. This project facilitates the identification of blood group even by common people who are unaware of the blood typing procedure.

scholarly journals An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques

2021 ◽  
Vol 11 (11) ◽  
pp. 5225
Author(s):  
Nuha Odeh ◽  
Anas Toma ◽  
Falah Mohammed ◽  
Yousef Dama ◽  
Farah Oshaibi ◽  
...  
Keyword(s):  
Image Matching ◽  
Human Error ◽  
Low Cost ◽  
Blood Type ◽  
Scale Invariant ◽  
Efficient System ◽  
Emergency Situations ◽  
Long Time ◽  
Speed Up Robust Feature ◽  
Matching Techniques

This paper presents a fast and accurate system to determine the type of blood automatically based on image processing. Blood type determination is important in emergency situations, where there is a need for blood transfusion to save lives. The traditional blood determination techniques are performed manually by a specialist in medical labs, where the result requires a long time or may be affected by human error. This may cause serious consequences or even endanger people’s lives. The proposed approach performs blood determination in real-time with low cost using any available mobile device equipped with a camera. A total of 500 blood samples were processed in this study using different image matching techniques including oriented fast and rotated brief (ORB), scale invariant feature transform (SIFT), and speed-up robust feature (SURF). The evaluation results show that our proposed system, which adopts the ORB algorithm, is the fastest and the most accurate among the state-of-the-art systems. It can achieve an accuracy of 99.6% in an average time of 250 ms.

Mechatronic System for ABO Human Blood Typing

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Vânia Moreira ◽  
José Machado ◽  
Vítor Carvalho ◽  
Filomena Soares ◽  
Ana Ferraz
Keyword(s):  
Image Processing ◽  
Response Time ◽  
Human Blood ◽  
Fast Response ◽  
Blood Type ◽  
Medical Emergency ◽  
Blood Typing ◽  
Image Processing Techniques ◽  
Emergency Situations ◽  
Processing Techniques

In medical emergency situations, when a patient needs a blood transfusion, the universal blood type O− is administered. This procedure may lead to the depletion of stock reserves of O− blood. Nowadays, there is no commercial equipment capable of determining the patient's blood type in situ, in a fast and reliable process. Human blood typing is usually performed through the manual test, which involves a macroscopic observation and interpretation of the results by an analyst. This test, despite of having a fast response time, may lead to human errors, which sometimes can be fatal to the patient. This paper presents the development of an automatic mechatronic prototype for determining human blood typing (ABO and Rh systems) through image processing techniques. The prototype design takes into account the characteristics of reliability of analysis, portability, and response time allowing the system to be used in emergency situations. The developed prototype performs blood and reagents mixture acquires the resultant image and processes the data (based on image processing techniques) to determine the sample blood type. It was tested in a laboratory, using cataloged samples of blood types, provided by the Portuguese Institute of Blood and Transplantation. Hereafter, it is expected to test and validate the prototype in clinical environments.

scholarly journals Pendeteksi Golongan Darah Manusia Berbasis Tensorflow menggunakan ESP32-CAM

2021 ◽  
Vol 9 (2) ◽  
pp. 359
Author(s):  
HUMMAM GHASSAN GHIFARI ◽  
DENNY DARLIS ◽  
ARIS HARTAMAN
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
Blood Sample ◽  
Blood Group ◽  
Human Blood ◽  
Blood Type ◽  
Ip Address ◽  
Human Blood Group ◽  
Group Detection ◽  
The Ideal

ABSTRAKPendeteksian golongan darah dilakukan untuk mengetahui golongan darah yang dimiliki. Hingga saat ini pendeteksian golongan darah masih dilakukan oleh petugas analis kesehatan menggunakan kemampuan mata manusia. Pada penelitian ini dilakukan perancangan alat pendeteksi golongan darah menggunakan ESP32-CAM. Alat ini menggunakan kamera OV2640 untuk menangkap citra, yang diproses menggunakan Tensorflow Object Detection API sebagai framework untuk melatih serta mengolah citra darah. Model latih akan digunakan pada kondisi pendeteksian langsung dan ditampilkan dalam bentuk jendela program golongan darah beserta tingkat akurasinya. Dalam penelitian ini pengujian dilakukan menggunakan 20 dataset dengan jarak pengukuran antara ESP32-CAM dengan citra golongan darah yaitu sejauh 20 cm. Hasil yang didapat selama pengujian mayoritas golongan darah yang dapat terdeteksi adalah golongan darah AB.Kata kunci: ESP32-CAM, Tensorflow, Python, Golongan Darah, Pengolahan Citra ABSTRACTBlood group detection is performed to determine the blood group. Currently, in detecting blood type, it still relies on the ability of the human eyeThis paper presents a human blood group detection device using ESP32-CAM. This tool uses ESP32-CAM to capture images, and the Tensorflow Object Detection API as a framework used to train and process an image. The way this tool works is that the ESP32-CAM will capture an image of the blood sample and then send it via the IP address. Through the IP Address, the python program will access the image, then the image will be processed based on a model that has been previously trained. The results of this processing will be displayed in the form of a window program along with the blood type and level of accuracy. In this study, testing was carried out based on the number of image samples, the number of datasets, and the measurement distance. The ideal measurement distance between the ESP32-CAM and the blood group image is 20 cm long. The results obtained during the testing of the majority of blood groups that can be detected are AB blood group.Keywords: ESP32-CAM, Tensorflow, Python, Blood Type, Image Processing

scholarly journals Blood Group Detection using Image Processing MATLAB

2021 ◽  
Vol 9 (VI) ◽  
pp. 5006-5013
Author(s):  
P. Hansik Sagar
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Blood Group ◽  
Human Error ◽  
Healthcare Applications ◽  
Group Detection ◽  
Canny Edge ◽  
Time Test ◽  
Processing Techniques ◽  
Blood Grouping

Blood grouping is one of the common and most essentiality for many of the major healthcare applications. Traditional way to determine the blood group involve human such as trained medical professionals which generally lead to human error. One of the solutions to overcome this issue is to automate and digitize this method. Image processing and computer vision techniques can be used for this purpose. Therefore, in this paper, we investigate the blood group detection using image processing techniques. For this purpose, experiment starts by taking images of sample blood slide as input and convert it into gray scale followed by binarization and canny edge detection. Finally, it decided the agglutination by counting detected edges. Performance of method is tested on real- time blood sample dataset. Experimental results show the accuracy of proposed method is comparable to real- time test.

scholarly journals Detection and Implementation of Blood Group and Hb Level by Image Processing Techniques

2021 ◽  
pp. 73-81
Keyword(s):  
Image Processing ◽  
Blood Transfusion ◽  
Blood Sample ◽  
Blood Group ◽  
Group Identification ◽  
Blood Groups ◽  
Detection Accuracy ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Clinical Experiments

In an emergency, an urgent blood transfusion from a person to the patient is required and blood group identification is the first process to do so. In addition, a hemoglobin test is often required to make decisions about blood transfusion as well as to check anemia. Hemoglobin testing is also required for complete blood count and monitoring a number of diseases. These blood tests are almost difficult in rural areas where lab facilities are not sufficient. Researchers proposed a number of methods to identify blood groups using computer vision techniques. However, no study was conducted to identify blood group and hemoglobin level in a work using image processing techniques and an android mobile application which shows high detection accuracy. In this paper, manual clinical experiments have been replaced by an android app using image processing techniques to detect blood groups and hemoglobin levels except users require using antigen before taking samples. The proposed technique is divided into two portions. The first portion is blood group detection, which is done by taking a blood sample and performing the grayscale conversion, binary conversion, segmentation, edge detection, and computation to make the decision. The second section describes how to determine hemoglobin levels by comparing a blood sample image to a hemoglobin color scale (HCS). Here, the Hemoglobin value is determined from their RGB values. It has been discovered that the proposed approaches are capable of detecting hemoglobin levels and blood groups in a cost-effective and error-free manner. As a result, the tests can be conducted in a remote area without adequate lab facilities and the proposed work can solve major steps in blood transfusion difficulties and anemia.

scholarly journals RGB Image Cluster Evaluation for Human Blood Group Identification by MLP Classification

2019 ◽  
Vol 8 (3) ◽  
pp. 4197-4202
Keyword(s):  
Blood Sample ◽  
Blood Group ◽  
Median Filter ◽  
Group Identification ◽  
Optimal Solution ◽  
Blood Groups ◽  
Blood Type ◽  
Blood Group System ◽  
Group System ◽  
Blood Exchange

Image processing is helping researchers to reach their goals in many ways, especially in medical fields. Blood organization is very important when it comes to receiving a blood exchange. The most important blood group identification method is ABO blood group system and the RhD blood group system. Blood groups are defined by the occupancy or preoccupied of a specific agglutinate on the get around of a red blood cell. Identifying the blood group is very important for medical treatment in pathological tests, at some point it gives us an inaccurate and also expensive result, therefore, to overcome these problems an efficient and optimal solution is required. The need for accurate detection is high in a disaster situation where there are no laboratory people or experts available to detect the type of it. In the proposed method, we have collected 50 blood sample images for each of 8 blood groups, total 400 blood sample images are considered for experimentation. In preprocessing, the median filter is used to eliminate noise from the blood images. Then these images are converted from RGB to grayscale conversion and also resizing of the images is carried out. Region based segmentation by using two methods Markov Random Field and Region Adjacency Graph are used for segmentation, texture, color, and shape features are extracted from segmented images. Hence this paper proposes a pixel cluster based analysis of the blood type based on the pixel analysis features. The overall accuracy of blood group determination is 93.85%.

IMPLEMENTATIONS EMERGENCY RESPONSE SYSTEM IN CASE OF ACCIDENTS AND EMERGENCY SITUATIONS (ERA-GLONASS) FOR CARS AND TRUCKS IN UZBEKISTAN

2020 ◽  
Author(s):  
Akmal Rustamov
Keyword(s):  
Emergency Response ◽  
Call Center ◽  
Emergency Services ◽  
Main Idea ◽  
Emergency Situation ◽  
High Mountain ◽  
Additional Information ◽  
Emergency Situations ◽  
Emergency Response System ◽  
Response System

The paper addresses the problem of increasing transportation safety due to usage of new possibilities provided by modern technologies. The proposed approach extends such systems as ERA-GLONASS and eCall via service network composition enabling not only transmitting additional information but also information fusion for defining required emergency means as well as planning for a whole emergency response operation. The main idea of the approach is to model the cyber physical human system components by sets of services representing them. The services are provided with the capability of self- contextualization to autonomously adapt their behaviors to the context of the car-driver system. The approach is illustrated via an accident emergency situation response scenario. “ERA-GLONASS” is the Russian state emergency response system for accidents, aimed at improving road safety and reducing the death rate from accidents by reducing the time for warning emergency services. In fact, this is a partially copied European e Call system with some differences in the data being transmitted and partly backward compatible with the European parent. The principle of the system is quite simple and logical: in the event of an accident, the module built into the car in fully automatic mode and without human intervention determines the severity of the accident, determines the vehicle’s location via GLONASS or GPS, establishes connection with the system infrastructure and in accordance with the protocol, transfers the necessary data on the accident (a certain distress signal). Having received the distress signal, the employee of the call center of the system operator should call the on-board device and find out what happened. If no one answers, send the received data to Sistema-112 and send it to the exact coordinates of the team of rescuers and doctors, and the last one to arrive at the place is given 20 minutes. And all this, I repeat, without the participation of a person: even if people caught in an accident will not be able to independently call emergency services, the data on the accident will still be transferred. In this work intended to add some information about applying system project in Uzbek Roads especially mountain regions like “Kamchik” pass. The Kamchik Pass is a high mountain pass at an elevation of 2.306 m above the sea level, located in the Qurama Mountains in eastern Uzbekistan and its length is about 88km.The road to reach the pass is asphalted, but there are rough sections where the asphalt has disappeared. It’s called A373. The old road over the pass was by passed by a tunnel built in 1999. On the horizon, the snow-capped peaks of the Fan Mountains come into view. The pass is located in the Fergana Valley between the Tashkent and Namangan Regions.

A Caution in Association of ABO Blood Group with COVID-19

2020 ◽  
Keyword(s):  
Gene Cluster ◽  
Clinical Outcomes ◽  
Blood Group ◽  
Association Analysis ◽  
Blood Type ◽  
Genome Wide Association ◽  
Chromosome 3 ◽  
Abo Blood Group ◽  
Genome Wide Association Analysis ◽  
Genome Wide

A comment on Zhao J, Yang Y, Huang H, Li D, Gu D, Lu X, et al. Association of ABO blood group and Covid19 susceptability. medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.03.11.20031096. Zeng X, Fan H, Lu D, Huang F, Meng X, Li Z, et al. Association between ABO blood group and clinical outcomes of Covid19. medRxiv[PREPRINT].2020; https://doi.org/10.1101/2020.04.15.20063107. Zietz M, Tatonetti N. Testing the association between blood type and COVID-19 infection, intubation, and death medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.04.08.20058073. Ellinghaus D, Degenhardt F, Bujanda L, al. e. The ABO blood group and a chromosome 3 gene cluster associate with SRAS-CoV2 respitarory failure in an Italy-Spain genome-wide association analysis. medRxiv. 2020; https://doi.org/10.1101/2020.05.31.20114991.

SISTEM PAKAR MENU MAKANAN SEHAT BAGI PENDERITA KOLESTEROL DENGAN METODE FORWARD CHAINING

2017 ◽  
Vol 3 (2) ◽  
pp. 72
Author(s):  
Gusnita Darmawati
Keyword(s):  
Expert System ◽  
Cholesterol Level ◽  
Blood Group ◽  
Healthy Food ◽  
Healthy Diet ◽  
Blood Type ◽  
Forward Chaining ◽  
Cholesterol Levels ◽  
Microsoft Office ◽  
Visual Basic 6.0

<p>Penelitian ini membangun suatu sistem pakar untuk menentukan menu makanan sehat berdasarkan golongan darah dan tingkat kadar kolesterol pasien dengan metode Forward Chaining. Tujuan untuk membantu orang awam dalam menentukan menu makanan sehat untuk pasien kolesterol. Sistem ini menganalisa masalah penentuan menu makanan sehat berdasarkan golongan darah dan tingkat kadar kolesterol pasien. Hasil yang diperoleh dari sitem pakar ini adalah berupa informasi makanan sehat yang akan dikonsumsi oleh pasien kolesterol dengan jenis golongan darah dan tingkat kadar kolesterol yang berbeda. Analisa dilakukan dengan cara mengetahui jenis golongan darah dan tingkat kadar kolesterol pasien yang ditampilkan oleh program sistem pakar ini, rancangan sistem ini menggunakan inference forward chaining, dengan implementasi sistem menggunakan sistem database Microsoft Office Access dan bahasa pemrograman Visual Basic 6.0. Dari rancangan aplikasi sistem pakar yang dibuat, maka orang awam yang memderita kolesterol dapat menentukan menu makanan sehat untuk di konsumsi berdasarkan golongan darah dan tingkat kadar kolesterol dengan menjalankan aplikasi sistem pakar.</p><p><em><br /></em></p><p><em><em>This study builds an expert system to determine the healthy food menu based on blood type and cholesterol levels of patients with Forward Chaining method. The goal is to help the layman in determining a healthy diet for cholesterol patients. This system analyzes the problem of determining healthy food menu based on blood group and patient cholesterol level. The results obtained from this expert system is in the form of healthy food information that will be consumed by cholesterol patients with the type of blood group and different cholesterol levels. From the design of expert system applications created, the layman who memderita cholesterol can determine the healthy diet to be consumed by blood type and cholesterol level by running an expert system application.<br /> <br /> </em></em></p>

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