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المؤلفSharif, Muhammad
المؤلفAmin, Javaria
المؤلفSiddiqa, Ayesha
المؤلفKhan, Habib Ullah
المؤلفMalik, Muhammad Sheraz Arshad
المؤلفAnjum, Muhammad Almas
المؤلفKadry, Seifedine
تاريخ الإتاحة2022-12-28T11:20:01Z
تاريخ النشر2020-09-03
اسم المنشورIEEE Access
المعرّفhttp://dx.doi.org/10.1109/ACCESS.2020.3021660
الاقتباسSharif, M., Amin, J., Siddiqa, A., Khan, H. U., Malik, M. S. A., Anjum, M. A., & Kadry, S. (2020). Recognition of different types of leukocytes using YOLOv2 and optimized bag-of-features. IEEE Access, 8, 167448-167459.
الرقم المعياري الدولي للكتاب2169-3536
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102781945&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37759
الملخصWhite blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.
اللغةen
الناشرIEEE
الموضوعCytoplasm
DarkNet-19
Leukocytes
Recognition
White blood cells
YOLOv2
العنوانRecognition of different types of leukocytes using YOLoV2 and optimized bag-of-features
النوعArticle
الصفحات167448-167459
رقم المجلد8
dc.accessType Open Access


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