A robust classification of acute lymphocytic leukemia-based microscopic images with supervised Hilbert-Huang transform.

التفاصيل البيبلوغرافية
العنوان:	A robust classification of acute lymphocytic leukemia-based microscopic images with supervised Hilbert-Huang transform.
المؤلفون:	Elrefaie RM; Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt., Mohamed MA; Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt., Marzouk EA; Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt., Ata MM; School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, 6th of October City, Giza, Egypt.; Department of Communications and Electronics Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt.
المصدر:	Microscopy research and technique [Microsc Res Tech] 2024 Feb; Vol. 87 (2), pp. 191-204. Date of Electronic Publication: 2023 Sep 15.
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9203012 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0029 (Electronic) Linking ISSN: 1059910X NLM ISO Abbreviation: Microsc Res Tech Subsets: MEDLINE
أسماء مطبوعة:	Original Publication: New York, NY : Wiley-Liss, c1992-
مواضيع طبية MeSH:	Algorithms* , Precursor Cell Lymphoblastic Leukemia-Lymphoma*, Child ; Humans ; Bayes Theorem ; Neural Networks, Computer ; Image Interpretation, Computer-Assisted/methods
مستخلص:	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.)
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فهرسة مساهمة:	Keywords: Bayesian regularization; LAB; acute lymphocytic leukemia; classification; empirical mode decomposition; k-means; neural networks
تواريخ الأحداث:	Date Created: 20230916 Date Completed: 20240112 Latest Revision: 20240112
رمز التحديث:	20240112
DOI:	10.1002/jemt.24425
PMID:	37715495
قاعدة البيانات:	MEDLINE

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تدمد:	1097-0029
DOI:	10.1002/jemt.24425