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Disease Detection using Blood Smear Analysis

by Pragati Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 7
Year of Publication: 2017
Authors: Pragati Sharma
10.5120/ijca2017916002

Pragati Sharma . Disease Detection using Blood Smear Analysis. International Journal of Computer Applications. 179, 7 ( Dec 2017), 41-44. DOI=10.5120/ijca2017916002

@article{ 10.5120/ijca2017916002,
author = { Pragati Sharma },
title = { Disease Detection using Blood Smear Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 179 },
number = { 7 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 41-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number7/28751-2017916002/ },
doi = { 10.5120/ijca2017916002 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:54:44.689760+05:30
%A Pragati Sharma
%T Disease Detection using Blood Smear Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 7
%P 41-44
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Blood smear analysis is an important diagnostic test which is performed to diagnose an array of diseases. The count of various blood cells and their morphological properties are the main focus of this test. Manual analysis of blood smears is time consuming and laborious. By automating this process and ultimately narrowing the scope of possible diseases, a considerable amount of time can be saved. This may in turn help medical staff as well as the patients. In this paper an automated technique for blood smear analysis using image processing is proposed to discern the blood cell count and blood cell properties. The results of image processing are then employed to generate a neuro-fuzzy system capable of predicting possible diseases.

References
  1. A. K. T. F. Mehdi Habibzadeh, “Application of Pattern Recognition Techniques for the Analysis of Blood Smear Images,” Jounal of Medical Informatics and Technologies, vol. 18, 2011.
  2. B.A.I. Khawaldeh, “Developing a Computer-based Information System to Improve the Diagnosis of Blood Anemia,” Middle East University, 2013.
  3. M.M.a.A.E. Mohammad Hamghalam, “Leukocyte segmentation in Giemsa-stained image of peripheral blood smears based on active contour,” in IEEE, 2009.
  4. C.D.R.Lorenzo Putzu,“White Blood Cells Identification and lassification from Leukemic Blood Image,” in International Work-Conference on Bioinformatics and Biomedical Engineering, 2013.
  5. S. S. Niranjan Chatap, “Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour,” IOSR Journal of Computer Engineering, vol. 16, no. 5, pp. 79-87, 2014.
  6. M. C. P. Thejashwini M, “Counting of RBC’s and WBC’s Using Image Processing Technique,” International Journal on Recent and Innovation Trends in Computing and Communication , vol. 3, no. 5, pp. 2948-2953, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Blood Smear Image Processing Neuro-Fuzzy System

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