{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:36:31Z","timestamp":1775068591462,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T00:00:00Z","timestamp":1701734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Northern Border University","award":["AMSA-2023-12-2012"],"award-info":[{"award-number":["AMSA-2023-12-2012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient\u2019s life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services.<\/jats:p>","DOI":"10.3390\/a16120556","type":"journal-article","created":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T02:55:32Z","timestamp":1701744932000},"page":"556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5456-5769","authenticated-orcid":false,"given":"A. Khuzaim","family":"Alzahrani","sequence":"first","affiliation":[{"name":"Department of Medical Laboratory Technology, Faculty of Medical Applied Science, Northern Border University, Arar 91431, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9811-0341","authenticated-orcid":false,"given":"Ahmed A.","family":"Alsheikhy","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7997-7038","authenticated-orcid":false,"given":"Tawfeeq","family":"Shawly","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"given":"Ahmed","family":"Azzahrani","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0613-4037","authenticated-orcid":false,"given":"Yahia","family":"Said","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.32604\/csse.2023.036985","article-title":"MayGAN: Mayfly optimization with generative adversarial network-based deep learning method to classify leukemia form blood smear images","volume":"42","author":"Veeraiah","year":"2023","journal-title":"Comput. 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