In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time‐consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best‐selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.

中文翻译：

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使用经典特征选择和 ELM 从血液病理学中识别血细胞疾病的自动化设计。

在人体免疫系统中，白细胞 (WBC) 会产生骨骼和淋巴肿块。这些细胞保护人体免受多种感染，如真菌和细菌。流行的 WBC 类型是嗜酸性粒细胞、淋巴细胞、中性粒细胞和单核细胞，它们是由专家手动诊断的。人工诊断过程复杂且耗时；因此，需要一个自动化系统来对这些 WBC 进行分类。在本文中，提出了一种使用特征选择和极限学习机 (ELM) 进行 WBC 分类的新方法。在第一步，执行数据增强以增加图像数量，然后实施新的对比度拉伸技术，名称为像素拉伸 (PS)。在下一步中，颜色和灰度大小区域矩阵 (GLSZM) 特征是从 PS 图像计算出来的，并基于高相似度水平融合到一个向量中。然而，也包含了一些影响分类性能的冗余特征。为了处理这个问题，实现了基于最大相关概率 (MRP) 的特征选择技术。在这项工作中，从适应度函数计算出的最佳选择特征是 ELM。所有最大相关性特征都被放到 ELM 中，这个过程一直持续到错误率最小。最后，通过 Cubic SVM 对最终选择的特征进行分类。为了验证所提出的方法，使用了 LISC 和 Dhruv 数据集，它达到了 96.60% 的最高准确率。从结果来看，