An improved classification technique is presented to identify automatically the acute lymphatic leukemia (ALL) subtypes. An adaptive segmentation procedure is performed on peripheral blood smear images to extract the main features (10 geometric features) from the segmented images of white blood cell (WBC), nucleus, and cytoplasm. To show the importance of the different extracted features for the diagnostic accuracy, a comprehensive study is made on all the possible permutation cases of the features using powerful classifiers which are K‐nearest neighbor (KNN) at different metric functions, support vector machine (SVM) with different kernels, and artificial neural network (ANN). This procedure enables us to construct a feature map depending only on least number of features which lead to the highest diagnostic accuracy. It is found that the features map regarding the vacuoles in the cytoplasm and the regularity of the nucleus membrane gives the highest accurate results. The automatic classification for ALL subtypes based only on these two effective features is assessed using the receiver operating characteristic (ROC) curve and F₁‐score measures. It is confirmed that the present technique is highly accurate, and saves the effort and time of training.

中文翻译：

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使用精细的简单分类准确自动检测急性淋巴细胞白血病。

提出了一种改进的分类技术来自动识别急性淋巴白血病 (ALL) 亚型。对外周血涂片图像执行自适应分割程序，以从白细胞 (WBC)、细胞核和细胞质的分割图像中提取主要特征（10 个几何特征）。为了显示不同提取特征对诊断准确性的重要性，使用强大的分类器对特征的所有可能排列情况进行了全面研究，这些分类器是不同度量函数下的 K-最近邻 (KNN)、支持向量机 (SVM) ) 具有不同的内核，以及人工神经网络 (ANN)。此过程使我们能够仅根据导致最高诊断准确性的最少特征数来构建特征图。发现关于细胞质中的液泡和细胞核膜的规律性的特征图给出了最高的准确结果。仅基于这两个有效特征的 ALL 亚型自动分类使用受试者工作特征 (ROC) 曲线和F _{1 -}得分措施。证实该技术具有高度准确度，并且节省了训练的精力和时间。