Blood Group Detection Using Image Processing


Authors : Bavyasri M; Elangovan K; Gayathree V; Mahanandha J; Althaf Ahamed S.A

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/msjfmtcu

Scribd : https://tinyurl.com/2s4j8jzp

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR2343

Abstract : During medical crises, access to an ample blood supply is crucial for saving lives. Shortages of required blood types in hospitals can result in significant delays in patient treatment. To tackle this urgent issue, we introduce BloodHub, an extensive web platform designed to streamline blood search, availability assessment, and compatibility determination using Support Vector Machines (SVM). BloodHub acts as a centralized hub where both donors and recipients can register and participate in the blood donation process. The platform offers a user-friendly interface for individuals in need of blood donations, enabling them to locate specific blood types nearby. Additionally, BloodHub provides real-time updates on blood unit availability across registered blood banks and donation centers, simplifying the procurement process for healthcare facilities and emergency responders. One of BloodHub's standout features is its SVM- powered blood group detection capability. By examining genetic markers in blood samples, SVM algorithms accurately identify donors' blood groups, ensuring compatibility with recipient needs. This functionality not only improves blood-matching efficiency but also reduces the risk of transfusion-related complications. Moreover, BloodHub implements robust security measures to protect user privacy and confidentiality.

Keywords : Blood Group Detection, Search Blood, Hospital Enrollment.

References :

  1. Garcia, M., & Rodriguez, A. (2019). "Machine Learning Techniques for Cancer Detection: A Review." International Journal of Bioinformatics Research, 8(1), 23-36.
  2. Wang, L., & Chen, Y. (2020). "Predictive Models for Heart Disease Diagnosis: A Comparative Study." Journal of Cardiology Informatics, 10(4), 145-158.
  3. Kim, S., & Lee, H. (2018). "Risk Prediction Models for Stroke: A Comparative Analysis." Journal of Neuroinformatic, 12(2), 65-78.
  4. Nguyen, T., & Tran, L. (2019). "Machine Learning Approaches for Alzheimer's Disease Prediction: A Comprehensive Review." Alzheimer's Research & Therapy, 6(1), 34-48.
  5. Tan, W., & Lim, K. (2020). "Forecasting Infectious Disease Outbreaks using Machine Learning: A Comparative Study." Journal of Epidemiology and Global Health, 18(3), 98-112.
  6. Patel, D., & Sharma, R. (2018). "Applications of Machine Learning in Drug Discovery: A Review." Drug Discovery Today, 14(4), 112-126.
  7. Chen, H., & Liu, Q. (2019). "Predicting Mortality in Intensive Care Units using Machine Learning Techniques: A Comparative Study." Journal of Critical Care Informatics,
  8. Kumar, P., & Dutta, M. K. (2019). Blood Group Detection from Smartphone Acquired Images Using Convolutional Neural Networks. In 2019 IEEE International Conference on Artificial Intelligence and Smart Systems (AIS2) (pp. 210- 215). IEEE.
  9. Rahman, M. S., Hossain, M. M., & Islam, M. Z. (2020). Blood group detection using image processing techniques. In 2020 7th International Conference on Networking, Systems and Security.
  10. Srivastava, S., Yadav, V., & Yadav, D. (2020). A comparative study of blood group detection using image processing and deep learning techniques. In 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) (pp. 509-514). IEEE.
  11. Gomes, A., Sharma, R., & Kumar, A. (2021). Blood group detection from microscope images using convolutional neural networks.
  12. Jaiswal, A., & Sharma, S. (2021). Blood group detection using image processing techniques: A review. In 2021 International Conference on Communication Systems, Computing and IT Applications (CSCI) (pp. 1-6). IEEE.
  13. Singh, A., & Mishra, S. (2022). An automated blood group detection system using image processing and machine learning techniques.
  14. Bhattacharyya, S., & Choudhury, A. (2022). Blood group detection from microscopic images using morphological operations and machine learning
  15. Rahman, M. A., Islam, M. Z., & Rahman, M. S. (2023). Automated blood group detection from microscopic images using deep learning. In the 2023 International Conference on Artificial Intelligence, Big Data, Computing, and Data Communication Systems
  16. Das, S., Das, S., & Bhowmik, T. (2023). A novel approach for blood group detection using image processing and feature extraction techniques. In 2023 3rd International Conference on Intelligent Sustainable Systems.
  17. Sharma, S., & Jain, A. (2023). Blood group detection using smartphone acquired images: A comparative study of machine learning techniques. In 2023 International Conference on Intelligent Sustainable Systems (ICISS).

During medical crises, access to an ample blood supply is crucial for saving lives. Shortages of required blood types in hospitals can result in significant delays in patient treatment. To tackle this urgent issue, we introduce BloodHub, an extensive web platform designed to streamline blood search, availability assessment, and compatibility determination using Support Vector Machines (SVM). BloodHub acts as a centralized hub where both donors and recipients can register and participate in the blood donation process. The platform offers a user-friendly interface for individuals in need of blood donations, enabling them to locate specific blood types nearby. Additionally, BloodHub provides real-time updates on blood unit availability across registered blood banks and donation centers, simplifying the procurement process for healthcare facilities and emergency responders. One of BloodHub's standout features is its SVM- powered blood group detection capability. By examining genetic markers in blood samples, SVM algorithms accurately identify donors' blood groups, ensuring compatibility with recipient needs. This functionality not only improves blood-matching efficiency but also reduces the risk of transfusion-related complications. Moreover, BloodHub implements robust security measures to protect user privacy and confidentiality.

Keywords : Blood Group Detection, Search Blood, Hospital Enrollment.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe