research-article

The Classification of Abnormal Red Blood Cell on The Minor Thalassemia Case Using Artificial Neural Network and Convolutional Neural Network

Authors:

Dyah Aruming Tyas,

Tri Ratnaningsih,

Sri HartatiAuthors Info & Claims

ICVIP '17: Proceedings of the International Conference on Video and Image Processing

Pages 228 - 233

https://doi.org/10.1145/3177404.3177438

Published: 27 December 2017 Publication History

Abstract

The morphological disorder of the red blood cell is one of the indications of a certain type of diseases. On the minor thalassemia, such cases like the erythrocyte having a nucleus, a few number of the fragment cell and the target cell will be seen. This research study aimed at classifying four types of abnormal blood based on the shape, texture, and colour which was obtained from the image of the peripheral blood smear. The preprocessing stage using histogram equalization, segmentation stage using morphological operation, until feature extraction had been done. On the classification stage, the best accuracy to classify the blood into five types using algorithm of momentum backpropagation neural network was 93.22%, while the result of classification using the convolutional neural network (CNN) was 92.55%.

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Cited By

Kodipalli APrajwal NB VRao TN GDevamane S(2024)Blood Test Report-Based Prediction and Classification of Diseases Using Artificial Neural Networks2024 First International Conference for Women in Computing (InCoWoCo)10.1109/InCoWoCo64194.2024.10863579(1-7)Online publication date: 14-Nov-2024
https://doi.org/10.1109/InCoWoCo64194.2024.10863579
Deshpande NGite SPradhan B(2024)Unlocking the Potential: Machine Learning and Deep Learning in Leukemia Diagnosis with Explainable AIIoT Sensors, ML, AI and XAI: Empowering A Smarter World10.1007/978-3-031-68602-3_12(201-258)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-68602-3_12
Dhar PSuganya Devi KSekar KSrinivasan P(2023)Morphology of Red Blood Cells Classification using Deep Learning Approach2023 IEEE 3rd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET)10.1109/TEMSMET56707.2023.10150054(1-6)Online publication date: 10-Feb-2023
https://doi.org/10.1109/TEMSMET56707.2023.10150054
Show More Cited By

Index Terms

The Classification of Abnormal Red Blood Cell on The Minor Thalassemia Case Using Artificial Neural Network and Convolutional Neural Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification

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ICVIP '17: Proceedings of the International Conference on Video and Image Processing

December 2017

272 pages

ISBN:9781450353830

DOI:10.1145/3177404

Copyright © 2017 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Nanyang Technological University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2017

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ICVIP 2017: International Conference on Video and Image Processing

December 27 - 29, 2017

Singapore, Singapore

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Cited By

Kodipalli APrajwal NB VRao TN GDevamane S(2024)Blood Test Report-Based Prediction and Classification of Diseases Using Artificial Neural Networks2024 First International Conference for Women in Computing (InCoWoCo)10.1109/InCoWoCo64194.2024.10863579(1-7)Online publication date: 14-Nov-2024
https://doi.org/10.1109/InCoWoCo64194.2024.10863579
Deshpande NGite SPradhan B(2024)Unlocking the Potential: Machine Learning and Deep Learning in Leukemia Diagnosis with Explainable AIIoT Sensors, ML, AI and XAI: Empowering A Smarter World10.1007/978-3-031-68602-3_12(201-258)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-68602-3_12
Dhar PSuganya Devi KSekar KSrinivasan P(2023)Morphology of Red Blood Cells Classification using Deep Learning Approach2023 IEEE 3rd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET)10.1109/TEMSMET56707.2023.10150054(1-6)Online publication date: 10-Feb-2023
https://doi.org/10.1109/TEMSMET56707.2023.10150054
K.T. NPrasad KSingh B(2022)Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological reviewMedical & Biological Engineering & Computing10.1007/s11517-022-02614-z60:9(2445-2462)Online publication date: 15-Jul-2022
https://doi.org/10.1007/s11517-022-02614-z
Deshpande NGite SAluvalu R(2021)Microscopic Analysis of Blood Cells for Disease Detection: A ReviewTracking and Preventing Diseases with Artificial Intelligence10.1007/978-3-030-76732-7_6(125-151)Online publication date: 15-Jul-2021
https://doi.org/10.1007/978-3-030-76732-7_6
Paz-Soto YHerold-Garcia SFernandes LDiaz-Matos S(2020)Automatic Classification of Erythrocytes Using Artificial Neural Networks and Integral Geometry-Based Functions2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI51738.2020.00029(156-163)Online publication date: Nov-2020
https://doi.org/10.1109/SIBGRAPI51738.2020.00029
Tyas DHartati SHarjoko ARatnaningsih T(2020)Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia CasesIEEE Access10.1109/ACCESS.2020.29831558(69849-69860)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2983155
Banerjee SChaudhuri S(2019)Total contribution score and fuzzy entropy based two‐stage selection of FC, ReLU and inverseReLU features of multiple convolution neural networks for erythrocytes detectionIET Computer Vision10.1049/iet-cvi.2018.554513:7(640-650)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5545

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