Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio
AbstractLeukemia is a type of cancer that affects the body’s blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique featu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2024.2304484 |
| _version_ | 1797348417898283008 |
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| author | Amani Al-Ghraibah Muhammad Al-Ayyad |
| author_facet | Amani Al-Ghraibah Muhammad Al-Ayyad |
| author_sort | Amani Al-Ghraibah |
| collection | DOAJ |
| description | AbstractLeukemia is a type of cancer that affects the body’s blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals. |
| first_indexed | 2024-03-08T12:04:38Z |
| format | Article |
| id | doaj.art-50e83dafad2c474ca2bda8412a585615 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-08T12:04:38Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-50e83dafad2c474ca2bda8412a5856152024-01-23T09:16:52ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2024.2304484Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratioAmani Al-Ghraibah0Muhammad Al-Ayyad1Medical Engineering Department, Al-Ahliyya Amman University, Amman, JordanMedical Engineering Department, Al-Ahliyya Amman University, Amman, JordanAbstractLeukemia is a type of cancer that affects the body’s blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals.https://www.tandfonline.com/doi/10.1080/23311916.2024.2304484Blood cancerdigital image processingleukemiamicroscopic imagesmicroscopic image’s featuresJin Zhongmin, Xian Jiao Tong University (China) and Leeds University.(UK), CHINA |
| spellingShingle | Amani Al-Ghraibah Muhammad Al-Ayyad Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio Cogent Engineering Blood cancer digital image processing leukemia microscopic images microscopic image’s features Jin Zhongmin, Xian Jiao Tong University (China) and Leeds University.(UK), CHINA |
| title | Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio |
| title_full | Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio |
| title_fullStr | Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio |
| title_full_unstemmed | Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio |
| title_short | Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio |
| title_sort | automated detection of leukemia in blood microscopic images using image processing techniques and unique features cell count and area ratio |
| topic | Blood cancer digital image processing leukemia microscopic images microscopic image’s features Jin Zhongmin, Xian Jiao Tong University (China) and Leeds University.(UK), CHINA |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2024.2304484 |
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