The Leukemia Healthy and Unhealthy Detection with Wavelet Transform Based On Co-Occurrence Matrix and Support Vector Machine

Leukemia is a malignant disease and belongs in a broader sense to Cancers. There are many types of leukemia, each of which requires specific treatment. Leukemia is almost one-third of all cancer deaths in children and young people. The most common type of leukemia in children is acute lymphoblastic leukemia (ALL). In this paper, a new approach is implanted on Leukemia ALL database. For the method the wavelet transform is used for feature extraction, the gray level co-occurrence matrix is used. Also, for classification, the SVM (Support Vector Machine) method is used. The proposed method is the best in applying the system designed to the Local Binary Pattern (LBP) and Histogram of Orientation (HOG) methods. This system aims to detect, diagnose, and verify leukemia cells from microscopic images to get high accuracy, efficiency, reliability, less processing time, smaller error, not complexity, fast, and easy to work. The system was built using microscopic images by examining changes in texture, colors, and statistical analysis. The success rate was 96.1667% for cancer data and 99.8833% for non-cancer data.

Erişime Açık
Görüntülenme
106
22.03.2022 tarihinden bu yana
İndirme
1
22.03.2022 tarihinden bu yana
Son Erişim Tarihi
12 Temmuz 2024 16:06
Google Kontrol
Tıklayınız
Tam Metin
Tam Metin İndirmek için tıklayın Ön izleme
Detaylı Görünüm
Eser Adı
(dc.title)
The Leukemia Healthy and Unhealthy Detection with Wavelet Transform Based On Co-Occurrence Matrix and Support Vector Machine
Yazar
(dc.contributor.author)
Cevat Rahebi
Yayın Yılı
(dc.date.issued)
2021
Tür
(dc.type)
Makale
Özet
(dc.description.abstract)
Leukemia is a malignant disease and belongs in a broader sense to Cancers. There are many types of leukemia, each of which requires specific treatment. Leukemia is almost one-third of all cancer deaths in children and young people. The most common type of leukemia in children is acute lymphoblastic leukemia (ALL). In this paper, a new approach is implanted on Leukemia ALL database. For the method the wavelet transform is used for feature extraction, the gray level co-occurrence matrix is used. Also, for classification, the SVM (Support Vector Machine) method is used. The proposed method is the best in applying the system designed to the Local Binary Pattern (LBP) and Histogram of Orientation (HOG) methods. This system aims to detect, diagnose, and verify leukemia cells from microscopic images to get high accuracy, efficiency, reliability, less processing time, smaller error, not complexity, fast, and easy to work. The system was built using microscopic images by examining changes in texture, colors, and statistical analysis. The success rate was 96.1667% for cancer data and 99.8833% for non-cancer data.
Açık Erişim Tarihi
(dc.date.available)
2021-07-24
Yayıncı
(dc.publisher)
Avrupa Bilim ve Teknoloji Dergisi
Dil
(dc.language.iso)
En
Konu Başlıkları
(dc.subject)
Leukemia
Konu Başlıkları
(dc.subject)
Wavelet transform
Konu Başlıkları
(dc.subject)
Image processing
Konu Başlıkları
(dc.subject)
Support Vector Machine
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.14081/1443
ISSN
(dc.identifier.issn)
2148-2683 / 2148-2683
Esere Katkı Sağlayan
(dc.contributor.other)
Salma ALBARGATHE
Esere Katkı Sağlayan
(dc.contributor.other)
Akram GIHEDAN
Esere Katkı Sağlayan
(dc.contributor.other)
Abdelhafid MOHAMED
Esere Katkı Sağlayan
(dc.contributor.other)
Mansur MOHAMED
Esere Katkı Sağlayan
(dc.contributor.other)
Tarek NAGEM
DOI
(dc.identifier.doi)
10.31590/ejosat.892170
Orcid
(dc.identifier.orcid)
0000-0001-9875-4860
Veritabanları
(dc.source.platform)
TR-Dizin
Analizler
Yayın Görüntülenme
Yayın Görüntülenme
Erişilen ülkeler
Erişilen şehirler
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.
Tamam

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms