急性淋巴细胞白血病（Acute lymphocytic leukemia，ALL）是一种恶性疾病，其特征是骨髓中原始细胞的发育，并迅速扩散到血液中。它主要影响儿童和60岁以上的个体。长期以来，传统的血液检测可能较为缓慢。通过自动化诊断，提高了在早期阶段识别ALL的可能性。本研究开发了一个改进的分类标准，将ALL显微图像分为两类：正常图像和原始图像。首先，采用创新的图像预处理技术以节省处理时间，进行数据的增强、增强和转换。还利用K-means聚类技术有效地从背景中分割相关细胞核。此外，基于Hilbert-Huang变换，利用经验模态分解（Empirical mode decomposition，EMD）提取最显著的特征。MATLAB函数，如主成分分析、灰度共生矩阵、局部二值模式、形状特征、离散余弦变换、离散傅里叶变换、离散小波变换和独立成分分析，已经被使用并与EMD进行对比。贝叶斯正则化（Bayesian regularization，BR）方法已应用于神经网络（Neural Networks，NNs）分类器中。除了NNs，还使用了支持向量机（Support Vector Machine），最近邻算法（K-Nearest Neighbors），随机森林（Random Forest），朴素贝叶斯（Naive Bayes），逻辑回归（Logistic Regression）和决策树（Decision Tree）等其他分类器，并与NNs进行了评估和对比。根据实验结果，基于ALL-IDB2（Image Database 2）数据集的NNs-based-EMD模型对对象进行了98.7%的准确分类，敏感性为99.3%，特异性为98.1%。研究亮点：在现有技术基础上，通过结合BR算法和神经网络分类器，实施一种用于分类正常和原始ALL图像的强大方法。通过数据增强和从RGB（红、绿、蓝）图像到LAB（亮度、A：色彩空间，B：色彩空间）图像的转换，进行强大的数据处理。利用K-means聚类从背景图像正确提取细胞核。利用HHT的现有技术中的EMD从分割图像中提取最显著的特征。© 2023 Wiley Periodicals LLC.

Acute lymphocytic leukemia (ALL) is a malignant condition characterized by the development of blast cells in the bone marrow and their quick dissemination into the bloodstream. It primarily affects children and individuals over the age of 60. Manual blood testing, which has been around for a long time, may be slow. The likelihood of recognizing ALL in its early stages was increased by automating the diagnosis. This research developed an improved criterion for classifying ALL microscopic images into two categories: normal images and blast images. First, to save processing time, innovative image preprocessing techniques were employed to gather data for data augmentation, enhancement, and conversion. The K-means clustering technique was also utilized to effectively segment the relevant nuclei from the background. Furthermore, the most salient features were extracted using an empirical mode decomposition (EMD) based on the Hilbert-Huang transform. MATLAB functions such as principal component analysis, gray level co-occurrence matrix, local binary pattern, shape features, discrete cosine transform, discrete Fourier transform, discrete wavelet transform, and independent component analysis have been used and compared with EMD. The Bayesian regularization (BR) method has been implemented in the neural networks (NNs) classifier. Along with NNs, other classifiers such as support vector machine, K-nearest neighbors, random forest, naive Bayes, logistic regression, and decision tree have been used, evaluated, and contrasted with NNs. According to experimental findings, the ALL-IDB2 (Image Database 2) dataset's NNs-based-EMD model classified objects with an accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%. RESEARCH HIGHLIGHTS: Implement a robust method for classifying normal and blast ALL images in the state of the art using the combination of the BR algorithm and the neural networks classifier. Perform robust data processing via data augmentation and conversion from RGB (Red, Green, and Blue) image LAB (Luminosity, A: color space, B: color space) image. Extract the nuclei correctly from the background image using k-means clustering. Extract the most salient features from the segmented images using EMD in the state of the art of HHT.© 2023 Wiley Periodicals LLC.