急性淋巴细胞白血病(ALL)是一种恶性疾病,其特征是骨髓中母细胞的发育及其快速扩散到血液中。它主要影响60岁以上的儿童和个人。手动验血,已经存在了很长时间,可能很慢。通过自动化诊断增加了早期识别ALL的可能性。这项研究开发了一种改进的标准,将所有显微图像分为两类:正常图像和爆炸图像。首先,为了节省处理时间,采用创新的图像预处理技术来收集数据以进行数据增强,增强,和转换。K均值聚类技术也被用来有效地从背景中分割相关的核。此外,使用基于希尔伯特-黄变换的经验模式分解(EMD)提取最显著的特征。主成分分析等MATLAB函数,灰度共生矩阵,本地二进制模式,形状特征,离散余弦变换,离散傅里叶变换,离散小波变换,和独立成分分析已被使用,并与EMD进行了比较。贝叶斯正则化(BR)方法已在神经网络(NN)分类器中实现。随着NN,其他分类器,如支持向量机,K-最近的邻居,随机森林,天真的贝叶斯,逻辑回归,决策树已经被使用,评估,并与NN形成对比。根据实验结果,ALL-IDB2(图像数据库2)数据集的基于NN的EMD模型以98.7%的准确率对对象进行分类,灵敏度为99.3%,特异性为98.1%。研究重点:使用BR算法和神经网络分类器的组合,实现对现有技术中的正常和爆炸所有图像进行分类的鲁棒方法。通过从RGB(红色,绿色,和蓝色)图像LAB(亮度,A:色彩空间,B:颜色空间)图像。使用k均值聚类从背景图像中正确提取细胞核。在HHT的现有技术中使用EMD从分割图像中提取最显著的特征。
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.