A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia
The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregul...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2022-01-01.
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| Online Access: | Get fulltext |
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| LEADER | 02712 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24975_15919 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Abas, Shakir Mahmood |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Abdulazeez, Adnan Mohsin |e author |
| 700 | 1 | 0 | |a Zeebaree, Diyar Qader |e author |
| 245 | 0 | 0 | |a A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia |
| 260 | |b Institute of Advanced Engineering and Science, |c 2022-01-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24975 | ||
| 520 | |a The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD). | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a CAD3; CNN; Leukemia; WBCs classification dataset; WBCs detection dataset; YOLO; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 25, No 1: January 2022; 200-213 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v25.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24975/15919 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24975/15919 |z Get fulltext |