{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T11:20:03Z","timestamp":1764156003786,"version":"3.46.0"},"reference-count":30,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Diabetes Mellitus (DM), including Type 1 and Type 2, is a metabolic disorder caused by defects in insulin secretion or action. Non\u2010invasive detection is more critical because invasive methods often lack data and have reduced accuracy, leading to poorer machine learning performance. This research proposes a new Octave\u2010CenterNet with DenseNet\u201077 framework for efficient detection and classification of diabetes from Volatile Organic Compounds (VOCs). The method combines a rapid discrete curvelet transform with wrapping to capture prominent features quickly, uses octave convolution to preserve high and low\u2010frequency patterns and enrich representations, employs CenterNet to detect acetone as a major biomarker, and leverages DenseNet\u201077 for gradient\u2010efficient classification. Willow sled catkin optimization adaptively fine\u2010tunes hyperparameters to further enhance performance. The model effectively distinguishes healthy individuals from diabetic patients and differentiates between Type 1 and Type 2 diabetes. Experimental results demonstrate excellent performance with 98.7% accuracy, 98% precision, 99.7% recall, and 99.34% F1 score, validating its robustness. Overall, this end\u2010to\u2010end, noise\u2010resistant, and computationally efficient framework offers a technically advanced and practical solution for non\u2010invasive diabetic detection.<\/jats:p>","DOI":"10.1002\/ima.70237","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T07:38:02Z","timestamp":1761723482000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non\u2010Invasive Diabetes Detection Through Human Breath Using Hybrid Octave\u2010\n                    <scp>CenterNet<\/scp>\n                    Neural Network With\n                    <scp>DenseNet<\/scp>\n                    \u201077 Model"],"prefix":"10.1002","volume":"35","author":[{"given":"R.","family":"Meena","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Science Easwari Engineering College  Chennai India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Vinu","sequence":"additional","affiliation":[{"name":"Department of Computing Technologies SRM Institute of Science and Technologies  Kattankulathur Tamil Nadu India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7409-8358","authenticated-orcid":false,"given":"J.","family":"Omana","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Vellore Institute of Technology  Chennai Tamil Nadu India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106773"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.microc.2023.109818"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s25051396"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/acssensors.4c02198"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.coelec.2021.100922"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.snb.2022.132182"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/dom.15944"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2023.124265"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2024.117061"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1021\/acssensors.4c01699"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.105998"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2023.101382"},{"key":"e_1_2_11_14_1","doi-asserted-by":"crossref","unstructured":"T.Yang C.Wen Q.Yang andY.Zhou \u201cGlucose Trend Prediction Model Based on Improved Wavelet Transform and Gated Recurrent Unit \u201d2023 https:\/\/doi.org\/10.21203\/rs.3.rs\u20102984141\/v1.","DOI":"10.21203\/rs.3.rs-2984141\/v1"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2023.1164655"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj\u2010cs.2082"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2024.03.016"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24041294"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.13005\/ojcst13.0203.03"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102919"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2023.3272482"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2024.3405995"},{"key":"e_1_2_11_23_1","doi-asserted-by":"crossref","unstructured":"H.Chen J.Chen Y.Xie et\u00a0al. \u201cOids\u201045: A Large\u2010Scale Benchmark Insect Dataset for Orchard Pest Monitoring \u201d2024 https:\/\/doi.org\/10.21203\/rs.3.rs\u20104339725\/v1.","DOI":"10.21203\/rs.3.rs-4339725\/v1"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21206936"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/e25010171"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010022\u201005570\u20108"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2023.3278278"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3359760"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e28720"},{"key":"e_1_2_11_30_1","doi-asserted-by":"crossref","unstructured":"H. A.Santoso N. S.Dewi A.Pambudi et\u00a0al. \u201cAdvancing non\u2010invasive glucose prediction with ontology\u2010integrated explainable AI \u201d2025 https:\/\/doi.org\/10.2139\/ssrn.5142403.","DOI":"10.2139\/ssrn.5142403"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12123062"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.70237","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T11:15:56Z","timestamp":1764155756000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.70237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":30,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/ima.70237"],"URL":"https:\/\/doi.org\/10.1002\/ima.70237","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"type":"print","value":"0899-9457"},{"type":"electronic","value":"1098-1098"}],"subject":[],"published":{"date-parts":[[2025,10,29]]},"assertion":[{"value":"2025-04-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70237"}}