Recognition of Blood Cancer Using Different Classification Techniques
Several image processing methods or applications have been established to get the requisite details from microscopic images for the fast and cost effective development of patient diagnosis. A type of leukemia that is more frequent in children is Acute Lymphoblastic Leukemia (ALL). The word 'Ac...
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| Format: | Artikel |
| Sprache: | English |
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International Transactions on Electrical Engineering and Computer Science
2022-09-01
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| Schriftenreihe: | International Transactions on Electrical Engineering and Computer Science |
| Schlagworte: | |
| Online Zugang: | https://iteecs.com/index.php/iteecs/article/view/4 |
| _version_ | 1827395946632183808 |
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| author | T. Swarna Latha |
| author_facet | T. Swarna Latha |
| author_sort | T. Swarna Latha |
| collection | DOAJ |
| description |
Several image processing methods or applications have been established to get the requisite details from microscopic images for the fast and cost effective development of patient diagnosis. A type of leukemia that is more frequent in children is Acute Lymphoblastic Leukemia (ALL). The word 'Acute' suggests that leukemia can develop rapidly and can lead to fatal death within a few months if not managed. Because of its nonspecific existence, erroneous diagnosis refers to the effects and manifestations of everything. And though it is challenging for hematologists to identify leukemia cells, manual sorting of blood cells is not only time-consuming but also unreliable. Consequently, early diagnosis of leukemia helps in presenting the patient with the necessary care. The mechanism recommends individuals in the blood picture the leucocytes from the blood cells as a response to this issue, and then select the lymphocyte cells. The morphological index of certain cells is measured and the involvement of leukemia is eventually classified. A literature review on several methods used to identify cancer cells has been carried out in this article.
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| first_indexed | 2024-03-08T18:42:24Z |
| format | Article |
| id | doaj.art-dd158faaf0ef434392777af4b0591d53 |
| institution | Directory Open Access Journal |
| issn | 2583-6471 |
| language | English |
| last_indexed | 2024-03-08T18:42:24Z |
| publishDate | 2022-09-01 |
| publisher | International Transactions on Electrical Engineering and Computer Science |
| record_format | Article |
| series | International Transactions on Electrical Engineering and Computer Science |
| spelling | doaj.art-dd158faaf0ef434392777af4b0591d532023-12-29T04:46:27ZengInternational Transactions on Electrical Engineering and Computer ScienceInternational Transactions on Electrical Engineering and Computer Science2583-64712022-09-01114Recognition of Blood Cancer Using Different Classification Techniques T. Swarna Latha0Department of Computer Science and Engineering, Narayana Engineering College, Nellore-524003, Andhra Pradesh, India. Several image processing methods or applications have been established to get the requisite details from microscopic images for the fast and cost effective development of patient diagnosis. A type of leukemia that is more frequent in children is Acute Lymphoblastic Leukemia (ALL). The word 'Acute' suggests that leukemia can develop rapidly and can lead to fatal death within a few months if not managed. Because of its nonspecific existence, erroneous diagnosis refers to the effects and manifestations of everything. And though it is challenging for hematologists to identify leukemia cells, manual sorting of blood cells is not only time-consuming but also unreliable. Consequently, early diagnosis of leukemia helps in presenting the patient with the necessary care. The mechanism recommends individuals in the blood picture the leucocytes from the blood cells as a response to this issue, and then select the lymphocyte cells. The morphological index of certain cells is measured and the involvement of leukemia is eventually classified. A literature review on several methods used to identify cancer cells has been carried out in this article. https://iteecs.com/index.php/iteecs/article/view/4Blood cancer detectionC-Means clusteringK-Means clusteringGenetic algorithm |
| spellingShingle | T. Swarna Latha Recognition of Blood Cancer Using Different Classification Techniques International Transactions on Electrical Engineering and Computer Science Blood cancer detection C-Means clustering K-Means clustering Genetic algorithm |
| title | Recognition of Blood Cancer Using Different Classification Techniques |
| title_full | Recognition of Blood Cancer Using Different Classification Techniques |
| title_fullStr | Recognition of Blood Cancer Using Different Classification Techniques |
| title_full_unstemmed | Recognition of Blood Cancer Using Different Classification Techniques |
| title_short | Recognition of Blood Cancer Using Different Classification Techniques |
| title_sort | recognition of blood cancer using different classification techniques |
| topic | Blood cancer detection C-Means clustering K-Means clustering Genetic algorithm |
| url | https://iteecs.com/index.php/iteecs/article/view/4 |
| work_keys_str_mv | AT tswarnalatha recognitionofbloodcancerusingdifferentclassificationtechniques |