E-ISSN 0976-2833 | ISSN 0975-3583
 

Research Article 


Automatic Tool Generation for Diagnosing Blood Anemia

K. S. Vijaya Lakshmi,K. Suvarna Vani.

Abstract
The development of new computer software techniques and erudition systems must emerge the creation of new
systems in several fields especially in the field of Bio-medical sciences, where new data frameworks have been made
to speak to innovative and initiating capacities to the degree application and uses are concerned. Sketch and carrying
out of a computer-based erudition operation by appropriating a detailed line of attack are bestowed for enhancing the
interpretation of blood anaemia. This approach incorporates a lot of strategies and procedures to investigate blood
misdirection images. Basically, a proficient strategy utilizing the picture handling procedure (picture upgrade,
division, and highlight taking out) has been developed which can be utilized to examine blood spread pictures
shouted with photomicroscope. Generally, blood spread imagery includes red blood cells, white blood cells, and
platelets. This system is used to classify the abnormal cells present in the blood sample. The framework checks the
general blood platelets and figures the level of various sorts of tallied anomalous cells like macrocyte, target cell,
howel-jolley body, sickle cell, elliptocyte, tear, spherocyte, etc. The presence of a few strange sorts and the related
rates show the kind of blood anaemia. The continuation of this strategy has a precision of 83%.

Key words: Blood Anaemia, Image Processing, Photo Microscope, Smear Images, Red Blood Cells


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by K. S. Vijaya Lakshmi
Articles by K. Suvarna Vani
on Google
on Google Scholar


How to Cite this Article
Pubmed Style

K. S. Vijaya Lakshmi,K. Suvarna Vani. Automatic Tool Generation for Diagnosing Blood Anemia. J Cardiovasc. Dis. Res.. 2021; 12(4): 126-134. doi: 10.31838/jcdr.2021.12.04.17


Web Style

K. S. Vijaya Lakshmi,K. Suvarna Vani. Automatic Tool Generation for Diagnosing Blood Anemia. http://www.jcdronline.org/?mno=90278 [Access: July 26, 2021]. doi: 10.31838/jcdr.2021.12.04.17


AMA (American Medical Association) Style

K. S. Vijaya Lakshmi,K. Suvarna Vani. Automatic Tool Generation for Diagnosing Blood Anemia. J Cardiovasc. Dis. Res.. 2021; 12(4): 126-134. doi: 10.31838/jcdr.2021.12.04.17



Vancouver/ICMJE Style

K. S. Vijaya Lakshmi,K. Suvarna Vani. Automatic Tool Generation for Diagnosing Blood Anemia. J Cardiovasc. Dis. Res.. (2021), [cited July 26, 2021]; 12(4): 126-134. doi: 10.31838/jcdr.2021.12.04.17



Harvard Style

K. S. Vijaya Lakshmi,K. Suvarna Vani (2021) Automatic Tool Generation for Diagnosing Blood Anemia. J Cardiovasc. Dis. Res., 12 (4), 126-134. doi: 10.31838/jcdr.2021.12.04.17



Turabian Style

K. S. Vijaya Lakshmi,K. Suvarna Vani. 2021. Automatic Tool Generation for Diagnosing Blood Anemia. Journal of Cardiovascular Disease Research, 12 (4), 126-134. doi: 10.31838/jcdr.2021.12.04.17



Chicago Style

K. S. Vijaya Lakshmi,K. Suvarna Vani. "Automatic Tool Generation for Diagnosing Blood Anemia." Journal of Cardiovascular Disease Research 12 (2021), 126-134. doi: 10.31838/jcdr.2021.12.04.17



MLA (The Modern Language Association) Style

K. S. Vijaya Lakshmi,K. Suvarna Vani. "Automatic Tool Generation for Diagnosing Blood Anemia." Journal of Cardiovascular Disease Research 12.4 (2021), 126-134. Print. doi: 10.31838/jcdr.2021.12.04.17



APA (American Psychological Association) Style

K. S. Vijaya Lakshmi,K. Suvarna Vani (2021) Automatic Tool Generation for Diagnosing Blood Anemia. Journal of Cardiovascular Disease Research, 12 (4), 126-134. doi: 10.31838/jcdr.2021.12.04.17





Most Viewed Articles
  • Massive pericardial effusion as the only manifestation of primary hypothyroidism
    Radheshyam Purkait , Anand Prasad , Ramchandra Bhadra , Arindam Basu
    J Cardiovasc. Dis. Res.. 2013; 4(4): 248-250
    » Abstract » doi: 10.1016/j.jcdr.2014.01.001

  • Impact of light exercises in selective cognitive response andhandballshooting accuracy performance in Mesopotamia handball players
    Ahuda Naji Zaidan, Qusay Mohammed Hamdan, Mohammed Kadhim Saleh, Samer Saadoun Abd El , Rida
    J Cardiovasc. Dis. Res.. 2021; 12(2): 141-145
    » Abstract » doi: 10.31838/jcdr.2021.12.02.18

  • Reduced nitrate level in individuals with hypertension and diabetes
    Shiekh Gazalla Ayub, Taha Ayub, Saquib Naveed Khan, Rubiya Dar, Khurshid Iqbal Andrabi
    J Cardiovasc. Dis. Res.. 2011; 2(3): 172-176
    » Abstract » doi: 10.4103/0975-3583.85264

  • Factor analysis of risk variables associated with metabolic syndrome in adult Asian Indians
    Mithun Das, Susil Pal, Arnab Ghosh
    J Cardiovasc. Dis. Res.. 2010; 1(2): 86-91
    » Abstract » doi: 10.4103/0975-3583.64442

  • Typical coronary artery aneurysm exactly within drug-eluting stent implantation region in a patient with rheumatoid arthritis
    Ying Zheng, Jing-yuan Mao
    J Cardiovasc. Dis. Res.. 2012; 3(4): 329-331
    » Abstract » doi: 10.4103/0975-3583.102725

  • Most Downloaded
  • Assessment of the Knowledge and Attitude of Male Students towards Smoking Based on Health Belief Model
    Rafat Rezapour-Nasrabad, Fatemeh Izadi, Atousa Karimi, Fateme Shariati Far, Khatereh Rostami, Amin Kiani, Afsaneh Ghasemi
    J Cardiovasc. Dis. Res.. 2020; 11(4): 116-121
    » Abstract » doi: 10.31838/jcdr.2020.11.04.20

  • Diabetic Retinopathy, The Automated of Detection of Retinal Fundus Images with Probabilistic Neural Networks (PNN)
    Elvina Amanda, Marischa Elveny, Rahmad Syah
    J Cardiovasc. Dis. Res.. 2020; 11(4): 302-306
    » Abstract » doi: 10.31838/jcdr.2020.11.04.54

  • Investigation of the Relationship between Social Support and Adherence to Treatment among Elderly Individuals with Type II Diabetes Mellitus
    Afsaneh Ghasemi, Rafat Rezapour-Nasrabad, Leila Nikrouz, Fatemeh Izadi, Atousa Karimi, Fateme Shariati Far, Zahra Khiali
    J Cardiovasc. Dis. Res.. 2020; 11(4): 122-129
    » Abstract » doi: 10.31838/jcdr.2020.11.04.21

  • Cardio-Vascular Disease Classification Using Stacked Segmentation Model and Convolutional Neural Networks
    G. Charlyn Pushpa Latha, S. Sridhar, S. Prithi, T. Anitha
    J Cardiovasc. Dis. Res.. 2020; 11(4): 26-31
    » Abstract » doi: 10.31838/jcdr.2020.11.04.05

  • The Prediction of the Bisoprolol Effectiveness in Patients with Stable Coronary Artery Disease with Post-Infarction Cardiosclerosis
    Svetlana S. Bunova, Ol'ga V. Zamahina, Nikolaj A. Nikolaev, Nina I.Zhernakova, Andrey A.Grishchenko
    J Cardiovasc. Dis. Res.. 2020; 11(4): 105-109
    » Abstract » doi: 10.31838/jcdr.2020.11.04.18

  • Most Cited Articles