Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features
Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features
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DOI：
10.1109/access.2020.3021660
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Kadry, Seifedine
中科院分区：
文献类型：
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作者：
Sharif, Muhammad;Amin, Javaria;Kadry, Seifedine
白色血细胞（WBC）保护人体免受不同类型的感染，包括真菌，寄生虫，病毒和细菌。白细胞中异常区域的检测是一项困难的任务。因此，提出了一种基于YOLOv 2-Nucleus-Cytoplasm的白细胞定位方法，该方法包含darkNet-19作为YOLOv 2模型的基础网络。在这个模型中，特征从darkNet-19的LeakyReLU-18中提取，并作为YOLOv 2模型的输入提供。YOLOv 2-核-细胞质模型使用最大评分标签对WBC进行定位和分类。它还将WBC定位到原始细胞和非原始细胞中。定位后，提取袋的特征，并使用粒子群优化算法（PSO）进行优化。改进的特征向量被馈送到分类器，优化朴素贝叶斯（O-NB）和优化判别分析（O-DA）用于WBC分类。实验在LISC、ALL-IDB 1和ALL-IDB 2数据集上进行。
White 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 naive Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.