Computer Department, Damietta University, Damietta, Egypt.
Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany.
Sci Rep. 2020 Feb 13;10(1):2536. doi: 10.1038/s41598-020-59215-9.
White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.
白细胞（WBC）白血病是由于骨髓中白细胞过度生成引起的，因此基于图像的恶性 WBC 检测对于白血病的检测非常重要。卷积神经网络（CNN）是目前用于这种图像分类的最先进技术，但它们的训练和部署的计算成本可能很高。我们在这里提出了一种改进的混合方法，用于高效分类 WBC 白血病。我们首先使用 VGGNet 从 WBC 图像中提取特征，VGGNet 是一种基于 ImageNet 预训练的强大 CNN 架构。然后，使用经过统计增强的沙蚕群算法（SESSA）对提取的特征进行过滤。这种受生物启发的优化算法选择最相关的特征，并去除高度相关和噪声特征。我们将提出的方法应用于两个公共的 WBC 白血病参考数据集，并实现了高准确性和降低的计算复杂性。SESSA 优化仅从 VGGNet 提取的 25K 个特征中选择了 1K 个，同时提高了准确性。结果在这些数据集上属于最佳水平，优于几种卷积网络模型。我们预计，CNN 特征提取和 SESSA 特征优化的组合可能对许多其他图像分类任务有用。
