White blood cells (WBCs) play a main role in identifying the health condition and disease characteristics of a normal person. An automated classification system is capable of recognizing white blood cells that may help doctors to diagnose several diseases like malaria, anemia, leukemia, etc. Automated blood cell analysis allows fast and accurate outcomes and often involves broad data without performance negotiation. The state-of-the-art systems use a lot of different stages (feature extraction, segmentation, pre-processing, etc.) to provide the automated blood cell analysis using blood smear images which is a lengthy process. To overcome these problems, this paper presents an efficient peripheral blood cell image recognition and classification using a combination of the salp swarm algorithm and the cat swarm optimization (SSPSO) algorithm-based optimized convolutional neural networks (SSPSO-CNN) method. This paper uses the CNN approach to classify five peripheral blood cells such as eosinophil, basophil, lymphocytes, monocytes, and neutrophils without any human intervention. The other objective of this paper is to propose an improved version of salp swarm optimizer (SSO) using particle swarm optimization (PSO) to attain competitive classification performance over the database of the blood cell images. In this paper, the CNN uses VGG19 architecture for training purposes. The accuracy of the classification achieved with VGG19 models is 98%. The proposed model based on the CNN approach optimized by SSPSO achieves high classification accuracy and provides automatic peripheral blood cell classification. This method establishes the fine-tuning process to develop a classifier trained using 10 674 images obtained from medical practice. The proposed method augmented the performance in terms of high precision and F1-score and obtained an overall classification accuracy of 99%.

中文翻译：

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CNN-SSPSO：一种用于外周血细胞图像识别和分类的混合优化 CNN 方法

白细胞 (WBC) 在识别正常人的健康状况和疾病特征方面发挥着主要作用。自动分类系统能够识别白细胞，可以帮助医生诊断疟疾、贫血、白血病等多种疾病。自动血细胞分析可以快速准确地得出结果，并且通常涉及广泛的数据而无需进行性能协商。最先进的系统使用许多不同的阶段（特征提取、分割、预处理等）来提供使用血涂片图像的自动化血细胞分析，这是一个漫长的过程。为了克服这些问题，本文提出了一种有效的外周血细胞图像识别和分类方法，它结合了 salp swarm 算法和基于猫群优化 (SSPSO) 算法的优化卷积神经网络 (SSPSO-CNN) 方法。本文使用 CNN 方法对嗜酸性粒细胞、嗜碱性粒细胞、淋巴细胞、单核细胞和中性粒细胞等五种外周血细胞进行分类，无需任何人为干预。本文的另一个目标是使用粒子群优化 (PSO) 提出一种改进版本的 salp 群优化器 (SSO)，以在血细胞图像数据库上获得有竞争力的分类性能。在本文中，CNN 使用 VGG19 架构进行训练。使用 VGG19 模型实现的分类准确率为 98%。所提出的基于 SSPSO 优化的 CNN 方法的模型实现了较高的分类精度，并提供了自动外周血细胞分类。该方法建立了微调过程，以开发使用从医学实践中获得的 10 674 张图像训练的分类器。所提出的方法在高精度和F1 分并获得 99% 的整体分类准确率。