Leukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

中文翻译：

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稀疏FCM和深度卷积神经网络用于急性淋巴细胞白血病的细分和分类。

提前进行白血病检测和诊断是减少急性淋巴细胞白血病（ALL）患者死亡人数的医学应用中的趋势主题。对于ALL的检测，必须分析采用了血液涂片图像的白细胞（WBC）。本文提出了一种新的急性淋巴细胞白血病的分类和分类技术。提出的自动白血病检测方法基于使用优化算法训练的深度卷积神经网络（Deep CNN），该算法名为“基于灰狼的Jaya优化算法”（GreyJOA），该算法是使用“灰狼优化器”（GWO）开发的和Jaya优化算法（JOA）可以提高全局收敛性。原来，输入图像将应用于预处理，并使用稀疏模糊C均值（Sparse FCM）聚类算法进行分割。然后，从预处理的输入图像的各段中提取诸如局部方向性模式（LDP）和基于颜色直方图的特征之类的特征。最后，将提取的特征应用于Deep CNN进行分类。使用ALL IDB2数据库的图像对该方法进行的实验评估表明，该方法获得的最大准确度，灵敏度和特异性分别为0.9350、0.9528和0.9389。提取的特征将应用于Deep CNN进行分类。使用ALL IDB2数据库的图像对该方法进行的实验评估表明，该方法获得的最大准确度，灵敏度和特异性分别为0.9350、0.9528和0.9389。提取的特征将应用于Deep CNN进行分类。使用ALL IDB2数据库的图像对该方法进行的实验评估表明，该方法获得的最大准确度，灵敏度和特异性分别为0.9350、0.9528和0.9389。