Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

<Previous Article In Volume
Next Article In Volume>

Diagnosing Microscopic Blood Samples for Early Detection of Leukemia by Deep and Hybrid Learning Techniques

Authors
Ebrahim Mohammed Senan1, *, Mukti E. Jadhav2, Ramesh R. Manza1, Vandana Bagal3
1Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India
2Shri Shivaji Science and Arts College, Chikhli District, Buldana, India
3K.K. Wagh Institute of Engineering Education and Research, Nasik, India
*Corresponding author. Email: senan1710@gmail.com
Corresponding Author
Ebrahim Mohammed Senan
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_4How to use a DOI?
Keywords
ALL; Machine learning; CNN; Hybrid method
Abstract

Blood is an important component of the human, which consists of many important components including White Blood Cells (WBC). Leukaemia is one of the dangerous kinds of cancer that affect the blood and bone marrow, affecting children and adults. Acute lymphoblastic Leukaemia (ALL) is dangerous and deadly type of blood cancer. Hematologists and experts work on diagnosing blood by taking patient samples and analyzing them with a high-quality magnifying lens. However, manual diagnosis is boring, time-consuming, and more prone to errors and differing expert views. Therefore, artificial intelligence techniques solve this problem and support the opinions of highly experienced experts. This research aims to develop diagnostic systems using a Convolutional Neural Network (CNN) and a hybrid CNN and SVM to diagnose the ALL_IDB2 dataset for early diagnosis of Leukaemia. CNN models and a hybrid technique consisting of two blocks were implemented, the first block of CNN models to extract feature and the second block, the SVM algorithm, to classify the feature. All the proposed systems achieved superior results in diagnosing the ALL_IDB2 dataset for early diagnosis of Leukaemia.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-196-8_4
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_4How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ebrahim Mohammed Senan
AU  - Mukti E. Jadhav
AU  - Ramesh R. Manza
AU  - Vandana Bagal
PY  - 2023
DA  - 2023/08/10
TI  - Diagnosing Microscopic Blood Samples for Early Detection of Leukemia by Deep and Hybrid Learning Techniques
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 23
EP  - 38
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_4
DO  - 10.2991/978-94-6463-196-8_4
ID  - Senan2023
ER  -