{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:15:30Z","timestamp":1777353330080,"version":"3.51.4"},"reference-count":35,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp;amp; Technique"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:label\/><jats:p>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\u2010means 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\u2010Huang transform. MATLAB functions such as principal component analysis, gray level co\u2010occurrence 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\u2010nearest neighbors, random forest, naive Bayes, logistic regression, and decision tree have been used, evaluated, and contrasted with NNs. According to experimental findings, the ALL\u2010IDB2 (Image Database 2) dataset's NNs\u2010based\u2010EMD model classified objects with an accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Research Highlights<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>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.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>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.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Extract the nuclei correctly from the background image using k\u2010means clustering.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Extract the most salient features from the segmented images using EMD in the state of the art of HHT.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/jemt.24425","type":"journal-article","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T02:25:46Z","timestamp":1694831146000},"page":"191-204","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A robust classification of acute lymphocytic leukemia\u2010based microscopic images with supervised Hilbert\u2010Huang transform"],"prefix":"10.1002","volume":"87","author":[{"given":"Reem Magdy","family":"Elrefaie","sequence":"first","affiliation":[{"name":"Department of Electronics and Communications Engineering, Faculty of Engineering Mansoura University  Mansoura Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed A.","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communications Engineering, Faculty of Engineering Mansoura University  Mansoura Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elsaid A.","family":"Marzouk","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communications Engineering, Faculty of Engineering Mansoura University  Mansoura Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4151-9717","authenticated-orcid":false,"given":"Mohamed Maher","family":"Ata","sequence":"additional","affiliation":[{"name":"School of Computational Sciences and Artificial Intelligence (CSAI) Zewail City of Science and Technology  6th of October City, Giza Egypt"},{"name":"Department of Communications and Electronics Engineering MISR Higher Institute for Engineering and Technology  Mansoura Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63754-9_7"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103341"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.03.024"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-020-02282-x"},{"key":"e_1_2_15_6_1","article-title":"Classification: fault diagnosis and feature categorization","volume":"110","author":"Baughman D. 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