Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods.

中文翻译：

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使用Euler Jenks优化多项式逻辑神经网络进行白细胞检测和分类

由于白细胞（WBC）在疾病诊断中的广泛接受，因此WBC的检测和分类成为热门话题。现有方法存在一些缺陷，例如错误程度高，准确性高，时限大和错误分类率高。提出了一种称为Jenks优化逻辑卷积神经网络（JO-LCNN）的WBC检测和分类方法。最初，Eulers主轴用作卷积模型，通过区分背景和RBC，然后消除它们而仅留下WBC，从而获得图像的旋转不变形式。通过消除不必要的特征，可以检测出固有特征，从而最大程度地降低了误分类率。综上所述，Jenks优化函数被用作合并模型，以获得较低分辨率的特征图。因此，JO-LCNN用于去除图像中的微小物体和完整的原子核。最后，利用多项式逻辑分类器通过损失函数对五类类别进行分类，并根据损失函数更新权重，从而以较高的准确率进行分类。使用LISC数据库对具有不同参数（如分类准确度，误报率和时间复杂度）的WBC进行。结果表明，与最新方法相比，JO-LCNN可以以更少的时间和错误分类率有效地提高准确性。使用LISC数据库对具有不同参数（如分类准确度，误报率和时间复杂度）的WBC进行。结果表明，与最新方法相比，JO-LCNN能够以更少的时间和错误分类率有效地提高准确性。使用LISC数据库对具有不同参数（如分类准确度，误报率和时间复杂度）的WBC进行。结果表明，与最新方法相比，JO-LCNN可以以更少的时间和错误分类率有效地提高准确性。