检测白血病特征的统计分析

Vandana Khobragade, Jagannath H. Nirmal, Aayesha Hakim
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摘要

在数字显微镜时代,图像处理、统计分析、分类和决策系统已成为医学诊断研究的重要工具。通过对图像进行可视化分析,临床医生可以发现细胞内结构的异常。白血病是一种癌症,其特征是异常白细胞(WBC)不受控制地增加。在血液涂片图像(BSI)中识别急性白血病肿瘤细胞是一项具有挑战性的任务。图像分割被认为是自动识别这种疾病的最重要步骤。本研究采用创新的基于凹面的分割算法来分割 ALLIDB2 数据库子图像中的白细胞。凹端点和椭圆特征用于凸形细胞图像的分割步骤。这一过程包括提取轮廓证据(检测每个物体的可见部分)和轮廓估计(对应于最终物体的轮廓)。通过基于凹度的分割识别细胞及其内部结构后,再根据细胞的形态和统计特征对细胞进行分类。我们使用一个公共数据集对该方法进行了评估,该数据集旨在测试分类和分割方法。统计工具 SPSS 用于独立检查衍生特征的重要性。在分类时,重要的特征会被导入机器学习技术,如支持向量机(SVM)、k-近邻(KNN)、神经网络(NN)、决策树(DT)和奈维贝叶斯(NB)。神经网络模型的 AUC 为 98.9%,总准确率为 95%,表现更好。基于神经网络模型的准确性,我们主张使用该模型来识别急性白血病细胞。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical Analysis of Features for Detecting Leukemia
In this age of digital microscopy, image processing, statistical analysis, categorization, and systems for decision-making have become essential tools for medical diagnostics research. By visualizing and analyzing images, clinicians can identify anomalies in intracellular structure. Leukemia is a cancerous condition marked by an unregulated increase in aberrant white blood cells (WBCs). Recognizing acute leukemia tumor cells in blood smear images (BSI) is a challenging assignment. Image segmentation is regarded as the most significant step in the automated identification of this disease. The innovative concavity-based segmentation algorithm is employed in this study to segment WBC in sub-images from the ALLIDB2 database. The concave endpoints and elliptical features are used in the segmentation step of convex-shaped cell images. The procedure involves the extraction of contour evidence, which detects the visible section of each object, and contour estimation, which corresponds to the final object’s contours. Following the identification of the cells and their internal structure by concavity-based segmentation, the cells are categorized based on their morphological and statistical features. The method was evaluated using a public dataset meant to test classification and segmentation approaches. The statistical tool SPSS is used to independently check the significance of derived features. For classification, significant features are passed into machine learning techniques such as support vector machines (SVM), k-nearest neighbor (KNN), neural networks (NN), decision trees (DT), and Nave Bayes (NB). With an AUC of 98.9% and a total accuracy of 95%, the neural network model performed better. We advocate using the neural network model to identify acute leukemia cells based on its accuracy.
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