{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:09:57Z","timestamp":1784030997100,"version":"3.55.0"},"reference-count":54,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":194,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Acute lymphoblastic leukaemia (ALL), a common form of cancer, remains a life\u2010threatening condition that affects individuals worldwide, including both adults and children. The prognosis is significantly worse when the disease is identified at an advanced stage, as the chances of successful treatment diminish. Diagnosis typically requires invasive and costly procedures, which can delay timely intervention. Images from peripheral blood smears (PBS) have been useful for the preliminary screening of ALL in probable cases. However, due to the nonspecific presentation of the disease, interpreting these images poses significant challenges, increasing the risk of misdiagnosis. We have proposed an approach that uses 20,000 PBS images to precisely enable the early diagnosis of ALL and its distinct subtypes (benign, malignant\u2010early Pro\u2010B, malignant\u2010Pro\u2010B, and malignant\u2010Pre\u2010B) in order to overcome these problems. Our proposed system uses colour normalized PBS images using Vahadane method along with the feature extraction capabilities of deep neural networks combined with a stacking ensemble learning approach that integrates various machine learning algorithms for classification. This system is capable of distinguishing ALL cases from haematogones and accurately identifying its subtypes. By streamlining the diagnostic process, the platform aims to reduce the effort and time required by clinicians and patients. Our proposed model highlight the system's effectiveness, with a comparative analysis of established machine learning algorithms showcasing its superior performance. The proposed model achieved exceptional accuracy (99.95%), recall (99.95%), precision (99.95%) and F1\u2010score (99.95%) for the early detection of ALL and its variants, indicating its potential as a proof\u2010of\u2010concept system, while requiring further validation before clinical\u00a0deployment.<\/jats:p>","DOI":"10.1049\/htl2.70091","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:58:16Z","timestamp":1784030296000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Ensemble Learning With Stain Normalization and Deep Feature Extraction for Acute Lymphoblastic Leukaemia Classification"],"prefix":"10.1049","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2981-1600","authenticated-orcid":false,"given":"Faysal","family":"Ahmmed","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering American International University\u2010Bangladesh (AIUB) Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3023-8457","authenticated-orcid":false,"given":"Md. Sadi Al","family":"Huda","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Khwaja Yunus Ali University Sirajganj Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Asraf","family":"Ali","sequence":"additional","affiliation":[{"name":"Khwaja Yunus Ali University Sirajganj Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1823-1304","authenticated-orcid":false,"given":"M. M. Manjurul","family":"Islam","sequence":"additional","affiliation":[{"name":"Ulster University Coleraine UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"29541","DOI":"10.1109\/ACCESS.2023.3260065","article-title":"Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning","volume":"11","author":"Jiwani N.","year":"2023","journal-title":"IEEE Access"},{"key":"e_1_2_10_3_1","volume-title":"Lymphoblastic Lymphoma","author":"Kaseb H.","year":"2023"},{"issue":"10","key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.3390\/healthcare10101812","article-title":"Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images","volume":"10","author":"Sampathila N.","year":"2022","journal-title":"Healthcare"},{"issue":"19","key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.3390\/electronics11193168","article-title":"A Deep Learning\u2010based approach for the Diagnosis of Acute Lymphoblastic Leukemia","volume":"11","author":"Saeed A.","year":"2022","journal-title":"Electronics"},{"issue":"1","key":"e_1_2_10_6_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40164-020-00170-6","article-title":"Leukemia Incidence Trends at the Global, Regional, and National Level Between 1990 and 2017","volume":"9","author":"Dong Y.","year":"2020","journal-title":"Experimental Hematology & Oncology"},{"issue":"2","key":"e_1_2_10_7_1","doi-asserted-by":"crossref","first-page":"e197","DOI":"10.1016\/S2214-109X(22)00501-0","article-title":"Global Estimates of Incidence and Mortality of Cervical Cancer in 2020: A Baseline Analysis of the WHO Global Cervical Cancer Elimination Initiative","volume":"11","author":"Singh D.","year":"2023","journal-title":"The Lancet Global Health"},{"key":"e_1_2_10_8_1","unstructured":"K.Stephens Every Month Delayed in Cancer Treatment Can Raise Risk of Death by Around 10\\%(BMJ 2020)."},{"issue":"1","key":"e_1_2_10_9_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1634\/theoncologist.2013-0290","article-title":"Barriers and Challenges to Global Clinical Cancer Research","volume":"19","author":"Seruga B.","year":"2014","journal-title":"The Oncologist"},{"issue":"6","key":"e_1_2_10_10_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1188\/02.ONF.988-996","article-title":"Beginning Treatment for Childhood Acute Lymphoblastic Leukemia: Insights From the Parents' Perspective","volume":"29","author":"McGrath P.","year":"2002","journal-title":"Oncology Nursing Forum"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","unstructured":"E. M.Lygizou M.Reiter M.Maurer\u2010Granofszky M.Dworzak andR.Grosu \u201cAutomated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia Using Set\u2010Transformers \u201d in2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)(IEEE 2024) 1\u20134.","DOI":"10.1109\/EMBC53108.2024.10781595"},{"issue":"3","key":"e_1_2_10_12_1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.4103\/2228-7477.186885","article-title":"Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K\u2010Means Clustering and Support Vector Machine","volume":"6","author":"Kazemi F.","year":"2016","journal-title":"Journal of Medical Signals and Sensors"},{"issue":"7","key":"e_1_2_10_13_1","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1093\/jnci\/djac030","article-title":"Rural\u2013Urban Disparities in Cancer Outcomes: Opportunities for Future Research","volume":"114","author":"Bhatia S.","year":"2022","journal-title":"Journal of the National Cancer Institute"},{"key":"e_1_2_10_14_1","doi-asserted-by":"crossref","unstructured":"S.PapiththiraandT.Kokul \u201cMelanoma Skin Cancer Detection Using EfficientNet and Channel Attention Module \u201d in2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS)(IEEE 2021) 227\u2013232.","DOI":"10.1109\/ICIIS53135.2021.9660759"},{"key":"e_1_2_10_15_1","doi-asserted-by":"crossref","unstructured":"S.Ali N.Javaid D.Javeed I.Ahmad A.Ali andU. M.Badamasi \u201cA Blockchain\u2010Based Secure Data Storage and Trading Model for Wireless Sensor Networks \u201d inAdvanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA\u20102020)(Springer 2020) 499\u2013511.","DOI":"10.1007\/978-3-030-44041-1_45"},{"key":"e_1_2_10_16_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.104987","article-title":"Mutual Information Based Hybrid Model and Deep Learning for Acute Lymphocytic Leukemia Detection in Single Cell Blood Smear Images","volume":"179","author":"Jha K. K.","year":"2019","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"15","key":"e_1_2_10_17_1","doi-asserted-by":"crossref","first-page":"5520","DOI":"10.3390\/s22155520","article-title":"BO\u2010ALLCNN: Bayesian\u2010Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images","volume":"22","author":"Atteia G.","year":"2022","journal-title":"Sensors"},{"key":"e_1_2_10_18_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102690","article-title":"Automated Blast Cell Detection for Acute Lymphoblastic Leukemia Diagnosis","volume":"68","author":"Khandekar R.","year":"2021","journal-title":"Biomedical Signal Processing and Control"},{"issue":"8","key":"e_1_2_10_19_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/int.22753","article-title":"A Fast and Efficient CNN Model for B\u2010ALL Diagnosis and its Subtypes Classification Using Peripheral Blood Smear Images","volume":"37","author":"Ghaderzadeh M.","year":"2022","journal-title":"International Journal of Intelligent Systems"},{"issue":"1","key":"e_1_2_10_20_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41375-021-01408-w","article-title":"Deep Learning Detects Acute Myeloid Leukemia and Predicts NPM1 Mutation Status From Bone Marrow Smears","volume":"36","author":"Eckardt J.\u2010N.","year":"2022","journal-title":"Leukemia"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","unstructured":"C.Mondal M. K.Hasan M. T.Jawad et\u00a0al. \u201cAcute Lymphoblastic Leukemia Detection From Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks \u201darXiv:2105.03995(2021).","DOI":"10.20944\/preprints202105.0429.v1"},{"issue":"11","key":"e_1_2_10_22_1","doi-asserted-by":"crossref","first-page":"7735","DOI":"10.1007\/s10489-021-02231-7","article-title":"A New Complete Color Normalization Method for H&E Stained Histopathological Images","volume":"51","author":"Vijh S.","year":"2021","journal-title":"Applied Intelligence"},{"key":"e_1_2_10_23_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103344","article-title":"Color Normalization of Faded H&E\u2010Stained Histological Images Using Spectral Matching","volume":"111","author":"Tosta T. A. A.","year":"2019","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"D.Ziaei W.Li S.Lam W.\u2010C.Cheng andW.Chen \u201cCharacterization of Color Normalization Methods in Digital Pathology Whole Slide Images \u201d inMedical Imaging 2020: Digital Pathology11320(SPIE 2020) 1132017.","DOI":"10.1117\/12.2550585"},{"key":"e_1_2_10_25_1","unstructured":"Hanahelaly \u201cBlood Cancer Dataset\u201d (2023) accessed February 4 2025 https:\/\/www.kaggle.com\/datasets\/hanahelalyy\/blood-cancer-dataset."},{"key":"e_1_2_10_26_1","doi-asserted-by":"crossref","unstructured":"M.Aria M.Ghaderzadeh D.Bashash H.Abolghasemi F.Asadi andA.Hosseini Acute Lymphoblastic Leukemia (ALL) Image Dataset(Kaggle 2021).","DOI":"10.1155\/2021\/9933481"},{"issue":"11","key":"e_1_2_10_27_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3514495","article-title":"Automated Analysis of Blood Smear Iimages for Leukemia Detection: A Comprehensive Review","volume":"54","author":"Mittal A.","year":"2022","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"e_1_2_10_28_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0169875","article-title":"Stain Deconvolution Using Statistical Analysis of Multi\u2010Resolution Stain Colour Representation","volume":"12","author":"Alsubaie N.","year":"2017","journal-title":"PLOS ONE"},{"key":"e_1_2_10_29_1","doi-asserted-by":"crossref","unstructured":"M.Macenko M.Niethammer J. S.Marron et\u00a0al. \u201cA Method for Normalizing Histology Slides for Quantitative Analysis \u201d in2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro(IEEE 2009) 1107\u20131110.","DOI":"10.1109\/ISBI.2009.5193250"},{"issue":"8","key":"e_1_2_10_30_1","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1109\/TMI.2016.2529665","article-title":"Structure\u2010Preserving Color Normalization and Sparse Stain Separation for Histological Images","volume":"35","author":"Vahadane A.","year":"2016","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"e_1_2_10_31_1","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1515\/jisys-2022-0198","article-title":"An Extensive Review of State\u2010of\u2010the\u2010Art Transfer Learning Techniques Used in Medical Imaging: Open Issues and Challenges","volume":"31","author":"Mukhlif A.","year":"2022","journal-title":"Journal of Intelligent Systems"},{"issue":"4","key":"e_1_2_10_32_1","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.jaad.2015.01.025","article-title":"Early Detection of Melanoma: Reviewing the ABCDEs","volume":"72","author":"Tsao H.","year":"2015","journal-title":"Journal of the American Academy of Dermatology"},{"issue":"11","key":"e_1_2_10_33_1","doi-asserted-by":"crossref","first-page":"2821","DOI":"10.3390\/diagnostics12112821","article-title":"Melanoma Detection by Non\u2010Specialists: An Untapped Potential for Triage?","volume":"12","author":"Cantisani C.","year":"2022","journal-title":"Diagnostics"},{"key":"e_1_2_10_34_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102329","article-title":"Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total\u2010Body Textured Meshes","volume":"77","author":"Zhao M.","year":"2022","journal-title":"Medical Image Analysis"},{"issue":"2","key":"e_1_2_10_35_1","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1245\/s10434-006-9240-4","article-title":"Method of Biopsy and Incidence of Positive Margins in Primary Melanoma","volume":"14","author":"Stell V. H.","year":"2007","journal-title":"Annals of Surgical Oncology"},{"key":"e_1_2_10_36_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114161","article-title":"Deep and Machine Learning Techniques for Medical Imaging\u2010Based Breast Cancer: A Comprehensive Review","volume":"167","author":"Houssein E. H.","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"e_1_2_10_37_1","first-page":"3","article-title":"Supervised machine learning: A review of classification techniques","volume":"160","author":"Kotsiantis S. B.","year":"2007","journal-title":"Emerging Artificial Intelligence Applied and Computational Engineering"},{"key":"e_1_2_10_38_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A Comprehensive Survey on Support Vector Machine Classification: Applications, Challenges and Trends","volume":"408","author":"Cervantes J.","year":"2020","journal-title":"Neurocomputing"},{"key":"e_1_2_10_39_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.patcog.2018.08.003","article-title":"A Novel Ensemble Method for K\u2010Nearest Neighbor","volume":"85","author":"Zhang Y.","year":"2019","journal-title":"Pattern Recognition"},{"key":"e_1_2_10_40_1","doi-asserted-by":"crossref","unstructured":"D.UmamaheswariandS.Geetha \u201cSegmentation and Classification of Acute Lymphoblastic Leukemia Cells Tooled With Digital Image Processing and ML Techniques \u201d inProceedings of the 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICICCS)(IEEE 2018) 1336\u20131341.","DOI":"10.1109\/ICCONS.2018.8662950"},{"key":"e_1_2_10_41_1","doi-asserted-by":"crossref","unstructured":"H. T.Madhloom S. A.Kareem andH.Ariffin \u201cA Robust Feature Extraction and Selection Method for the Recognition of Lymphocytes Versus Acute Lymphoblastic Leukemia \u201d inProceedings of the International Conference on Advanced Computer Science Applications and Technologies (ACSAT)(IEEE 2012) 330\u2013335.","DOI":"10.1109\/ACSAT.2012.62"},{"issue":"1","key":"e_1_2_10_42_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random Forest Classifier for Remote Sensing Classification","volume":"26","author":"Pal M.","year":"2005","journal-title":"International Journal of Remote Sensing"},{"key":"e_1_2_10_43_1","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.bspc.2016.11.021","article-title":"Gray Level Co\u2010Occurrence Matrix and Random Forest Based Acute Lymphoblastic Leukemia Detection","volume":"33","author":"Mishra S.","year":"2017","journal-title":"Biomedical Signal Processing and Control"},{"key":"e_1_2_10_44_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.bspc.2018.08.012","article-title":"Texture Feature Based Classification on Microscopic Blood Smear for Acute Lymphoblastic Leukemia Detection","volume":"47","author":"Mishra S.","year":"2019","journal-title":"Biomedical Signal Processing and Control"},{"key":"e_1_2_10_45_1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support\u2010Vector Networks","volume":"20","author":"Cortes C.","year":"1995","journal-title":"Machine Learning"},{"issue":"5","key":"e_1_2_10_46_1","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/72.788645","article-title":"Support Vector Machines for Spam Categorization","volume":"10","author":"Drucker H.","year":"1999","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"10","key":"e_1_2_10_47_1","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1002\/jemt.22718","article-title":"Computer Aided Detection and Classification of Acute Lymphoblastic Leukemia Cell Subtypes Based on Microscopic Image Analysis","volume":"79","author":"MoradiAmin M.","year":"2016","journal-title":"Microscopic Research and Technique"},{"issue":"1","key":"e_1_2_10_48_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1108\/IJCS-10-2019-0029","article-title":"RegBoost: A Gradient Boosted Multivariate Regression Algorithm","volume":"4","author":"Li W.","year":"2020","journal-title":"International Journal of Crowd Science"},{"key":"e_1_2_10_49_1","doi-asserted-by":"crossref","first-page":"137333","DOI":"10.1109\/ACCESS.2024.3446765","article-title":"Text Regression Analysis: A Review, Empirical, and Experimental Insights","volume":"12","author":"Taha K.","year":"2024","journal-title":"IEEE Access"},{"key":"e_1_2_10_50_1","doi-asserted-by":"crossref","unstructured":"R. K.AminandY.Sibaroni \u201cImplementation of Decision Tree Using C4.5 Algorithm in Decision Making of Loan Application by Debtor (Case Study: Bank Pasar of yogyakarta Special Region) \u201d inProceedings of the 3rd International Conference on Information and Communication Technology (ICoICT)(Springer 2015):75\u201380.","DOI":"10.1109\/ICoICT.2015.7231400"},{"issue":"22","key":"e_1_2_10_51_1","first-page":"41","article-title":"An Empirical Study of the Naive Bayes Classifier","volume":"3","author":"Rish I.","year":"2001","journal-title":"Proceedings of the IJCAI Workshop Empirical Methods Artificial Intelligence"},{"key":"e_1_2_10_52_1","doi-asserted-by":"crossref","DOI":"10.1038\/srep14938","article-title":"An Intelligent Decision Support System for Leukaemia Diagnosis Using Microscopic Blood Images","volume":"5","author":"Neoh S. C.","year":"2015","journal-title":"Scientific Reports"},{"issue":"3","key":"e_1_2_10_53_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.measurement.2014.04.008","article-title":"Automatic Segmentation, Counting, Size Determination and Classification of White Blood Cells","volume":"55","author":"Nazlibilek S.","year":"2014","journal-title":"Measurement"},{"key":"e_1_2_10_54_1","doi-asserted-by":"crossref","first-page":"48699","DOI":"10.1109\/ACCESS.2019.2910277","article-title":"iDTTi\u2010CSsmoteB: Identification of Drug\u2013Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost with Over\u2010Sampling Technique SMOTE","volume":"7","author":"Mahmud S. M. H.","year":"2019","journal-title":"IEEE Access"},{"key":"e_1_2_10_55_1","doi-asserted-by":"crossref","unstructured":"C.Rozikin A.Buono C.Arif S.Wahjuni andWidodo \u201cClassification of the Severity of Downy Mildew Disease Using LGBM \u201d in2023 International Conference on Informatics Multimedia Cyber and Informations System (ICIMCIS)(IEEE 2023) 364\u2013368.","DOI":"10.1109\/ICIMCIS60089.2023.10348974"}],"container-title":["Healthcare Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl2.70091","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/htl2.70091","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl2.70091","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:58:24Z","timestamp":1784030304000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/htl2.70091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1049\/htl2.70091"],"URL":"https:\/\/doi.org\/10.1049\/htl2.70091","archive":["Portico"],"relation":{},"ISSN":["2053-3713","2053-3713"],"issn-type":[{"value":"2053-3713","type":"print"},{"value":"2053-3713","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2026-01-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70091"}}