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ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network

Bhavana R. Maale1 , Soumya 2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-7 , Page no. 230-234, Jul-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i7.230234

Online published on Jul 31, 2019

Copyright © Bhavana R. Maale, Soumya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Bhavana R. Maale, Soumya, “ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.230-234, 2019.

MLA Style Citation: Bhavana R. Maale, Soumya "ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network." International Journal of Computer Sciences and Engineering 7.7 (2019): 230-234.

APA Style Citation: Bhavana R. Maale, Soumya, (2019). ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network. International Journal of Computer Sciences and Engineering, 7(7), 230-234.

BibTex Style Citation:
@article{Maale_2019,
author = {Bhavana R. Maale, Soumya},
title = {ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2019},
volume = {7},
Issue = {7},
month = {7},
year = {2019},
issn = {2347-2693},
pages = {230-234},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4750},
doi = {https://doi.org/10.26438/ijcse/v7i7.230234}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i7.230234}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4750
TI - ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network
T2 - International Journal of Computer Sciences and Engineering
AU - Bhavana R. Maale, Soumya
PY - 2019
DA - 2019/07/31
PB - IJCSE, Indore, INDIA
SP - 230-234
IS - 7
VL - 7
SN - 2347-2693
ER -

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Abstract

In the modern times digital image processing technology used by the end users has been in the interest as it provides the easy solution to the complicated issues. Like face recognition, image classification etc. We have proposed the concept of blood group type detection using image processing techniques based on the input images. It will be very difficult to detect the type of the blood to any end user. The need of the accurate detection is high in disaster situation where no lab or expert persons are available to detect the type of it. Hence we have proposed a pixel cluster based analysis of the blood type based on the Region Adjacency Graphs (RAG) and Super Resolution Mapping (SRM) with pixel analysis and Region of interest (ROI) based image segmentation. Later the use of neural network will help to classify the image based on the pixel analysis features. The proposed system results were obtained by using MATLAB. Successful results were obtained and accuracy of the proposed system is most desirable.

Key-Words / Index Term

Blood type detection, Image segmentation, Pixel analysis, Neural Network

References

[1] Osareh, Alireza, “Automated identifaction of diabetic retinal exudates in digital color images”, British Journal of Ophthalmology 87.10, pp.1220-1223, 2013.
[2] Yun, Wong Li and Achary, U Rajendra and Venkatesh, YV and Chee, Caroline and Min, Lim Choo and Ng, EYK, “Identification of different stages of diabetic retinopathy using retinal otical images”, Information Sciences 178.1, pp.106-121, 2008.
[3] E. A. Henneman, G. S. Avrunin, L. A. Clarke, L. J. Osterweil, C. Jr. Andrzejewski, K. Merrgan, R. Cobleigh, K. Frederick, E. Katz-Bassett, P. L. Henneman, “Increasing patient safty and effiency in transfusion therpy using formal process defamations”, Transfuse Med Rev, Vol. 21, pp. 49-57, 2007.
[4] F. Ana, C. Vitor, S. Filomena and L. P. Celina, “Characterization of Blood Samples Using Image Processing Techniques”, Sensors & Actuators: A. Physical(impact factor: 1.674).
[5] Ferraz, Ana, “Automatic system for determination of blood types using image processing techniques”, Bioengineering (ENBENG), 2013 IEEE 3rd Portuguese Meeting in. IEEE, 2013
[6] Abubakar Yamin, Faisal Imran, Usman Akbar, Syed Hassan Tanvir, “Image Processing Based Detection and Classification of Blood Group Using Color Images”, Internatinal Conference on Communication, Computing and Digital Systems (C-CODE), pp. 293-298, 2017
[7] ]P. Sturgeon, "Automation: its introduction to tbe field of blood group serology", Immunohematology Journal of Blood Group Serology and Education, Vol. 17, . 4, 2001.
[8] A. Dada, D. Beck, G. Schmitz., "Automation and Data Processing in Blood Banking Using the Ortho Autocued In nova System", Transfusion Medicine Hemotherapy, Vol. 34, pp. 341-346, 2007.
[9] S. Y. Shin, K. C. Kwon, S. H. koo, J. W. Park, C. S. Ko,J. H. Song, J. Y. Sung, "Evaluation of two automated instruments for pre-transfusion testing: Auto VueInnova and Techno Twin Station", Korean j Lab Med., Vol. 3, pp. 214-220, Jun.2008.
[10] G. Wittmann, J. Frank, W. Schram, M. Spannagl,"Automation and Data Processing with tbe Immucor Galileo System in a University Blood Bank", Transfusion Medicine Hemotherapy. Vol. 34, pp. 347-352, 2007.
[11] A. Dada, D. Beck, G. Schmitz, "Automation and Data Processing in Blood Banking Using the Ortho AutoVue Innova System". Transfusion Medicine Hemotherapy, Vol. 34, pp. 341-346, 2007.