{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:45Z","timestamp":1787027925251,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T00:00:00Z","timestamp":1674345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007614","name":"Al Jouf University","doi-asserted-by":"publisher","award":["DSR-2021-02-0206"],"award-info":[{"award-number":["DSR-2021-02-0206"]}],"id":[{"id":"10.13039\/501100007614","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The immune system\u2019s overproduction of white blood cells (WBCs) results in the most common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the primary causes of death worldwide. The most well-known type of leukemia found in the human bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early and precise cancer detection. As a result, doctors can use computer-aided diagnostic (CAD) models to detect early leukemia effectively. In this research, we proposed a classification model based on the EfficientNet-B3 convolutional neural network (CNN) model to distinguish ALL as an automated model that automatically changes the learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at the beginning of each epoch. We evaluated the proposed model on the C-NMC_Leukemia dataset. The dataset was pre-processed with normalization and balancing. The proposed model was evaluated and compared with recent classifiers. The proposed model\u2019s average precision, recall, specificity, accuracy, and Disc similarity coefficient (DSC) were 98.29%, 97.83%, 97.82%, 98.31%, and 98.05%, respectively. Moreover, the proposed model was used to examine microscopic images of the blood to identify the malaria parasite. Our proposed model\u2019s average precision, recall, specificity, accuracy, and DSC were 97.69%, 97.68%, 97.67%, 97.68%, and 97.68%, respectively. Therefore, the evaluation of the proposed model showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing models.<\/jats:p>","DOI":"10.3390\/diagnostics13030404","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T03:48:08Z","timestamp":1674445688000},"page":"404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6888-5114","authenticated-orcid":false,"given":"Sameh","family":"Abd El-Ghany","sequence":"first","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2504-6051","authenticated-orcid":false,"given":"Mohammed","family":"Elmogy","sequence":"additional","affiliation":[{"name":"Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4846-101X","authenticated-orcid":false,"given":"A. A. Abd","family":"El-Aziz","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5140148","DOI":"10.1155\/2022\/5140148","article-title":"Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method","volume":"2022","author":"Abir","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101661","DOI":"10.1016\/j.media.2020.101661","article-title":"SDCT-auxNet\u03b8: DCT Augmented Stain Deconvolutional CNN with Auxiliary Classifier For Cancer Diagnosis","volume":"61","author":"Gehlot","year":"2020","journal-title":"Med. 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