{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:25:44Z","timestamp":1776216344573,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"vor","delay-in-days":3,"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":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>In our research, we introduce a sophisticated \u201ctwo\u2010stage\u201d or cascade model designed to enhance the precision of lung nodule analysis. This innovative approach integrates two crucial processes: detection and segmentation. In the initial stage, a specialized object detection algorithm efficiently scans medical images to identify potential areas of interest, specifically focusing on lung nodules. This plays a crucial role in minimizing the segmentation area, particularly in the context of lung imaging, where the structures exhibit heterogeneity. This algorithm helps focus the segmentation process only on the relevant areas, reducing unnecessary computation and potential errors. Subsequently, the second stage employs advanced segmentation algorithms to precisely delineate the boundaries of the identified nodules, providing detailed and accurate contours. The combination of object detection and segmentation not only enhances the overall accuracy of lung cancer detection but also minimizes false positives, streamlines the workflow for radiologists, and provides a more comprehensive understanding of potential abnormalities. Additionally, it improves the efficiency and accuracy of segmentation, especially in cases where the complexity and heterogeneity of the lung structure make the segmentation task more challenging. This proposed method has been tested on the LIDC\u2010IDRI dataset, demonstrating favorable results in both nodule detection and segmentation steps, with 81.3% mAP and 83.54% DSC, respectively. These results serve as evidence that the proposed method effectively improves the accuracy of lung nodule detection and segmentation.<\/jats:p>","DOI":"10.1002\/ima.70023","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T08:59:12Z","timestamp":1735981152000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Cascade Model to Detect and Segment Lung Nodule Using <scp>YOLOv8<\/scp> and <scp>Resnet50U<\/scp>\u2010Net"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2381-5708","authenticated-orcid":false,"given":"Selma","family":"Mammeri","sequence":"first","affiliation":[{"name":"Laboratory of Mathematics, Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University  Tebessa Algeria"}]},{"given":"Mohamed\u2010Yassine","family":"Haouam","sequence":"additional","affiliation":[{"name":"Laboratory of Mathematics, Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University  Tebessa Algeria"}]},{"given":"Mohamed","family":"Amroune","sequence":"additional","affiliation":[{"name":"National School of Nanoscience and Nanotechnology, Sidi Abdallah Algiers Algeria, Laboratory of Mathematics, Informatics and Systems (LAMIS)  Mahelma Algeria"}]},{"given":"Issam","family":"Bendib","sequence":"additional","affiliation":[{"name":"Laboratory of Mathematics, Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University  Tebessa Algeria"}]},{"given":"Elhadj","family":"Benkhelifa","sequence":"additional","affiliation":[{"name":"Staffordshire University  Stoke\u2010on\u2010Trent UK"}]}],"member":"311","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21747-0_4"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2015.231"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127445"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122672"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP52935.2021.9522605"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2004.09.035"},{"issue":"21","key":"e_1_2_9_8_1","first-page":"147","article-title":"Lung Cancer Detection Using Image Processing Techniques","volume":"11","author":"Al\u2010Tarawneh M. S.","year":"2012","journal-title":"Leonardo Electronic Journal of Practices and Technologies"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2007.907555"},{"key":"e_1_2_9_10_1","first-page":"395","article-title":"Automatic Ct Image Segmentation of the Lungs With Region Growing Algorithm","author":"Mesanovic N.","year":"2011","journal-title":"18th International Conference on Systems, Signals and Image Processing\u2010IWSSIP"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.5815\/ijigsp.2014.01.01"},{"issue":"17","key":"e_1_2_9_12_1","first-page":"2948","article-title":"Morphological Techniques for Medical Images: A Review","volume":"4","author":"Irum I.","year":"2012","journal-title":"Research Journal of Applied Sciences, Engineering and Technology"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOSCI.2016.7845300"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"O.Ronneberger P.Fischer andT.Brox \u201cU\u2010Net: Convolutional Networks for Biomedical Image Segmentation \u201d 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16895-5"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2018.08.086"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/math9131457"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e17599"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-48004-8"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105934"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2019.06.003"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.014"},{"key":"e_1_2_9_23_1","unstructured":"E.KopelowitzandG.Engelhard \u201cLung Nodules Detection and Segmentation Using 3D Mask\u2010RCNN \u201d 2019 arXiv preprint arXiv:1907.07676."},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.05.024"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105781"},{"key":"e_1_2_9_26_1","first-page":"1","article-title":"Design of Lung Nodules Segmentation and Recognition Algorithm Based on Deep Learning","volume":"22","author":"Hui Y.","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16864-y"},{"key":"e_1_2_9_28_1","unstructured":"G.Samuel I. I. I.Armato G.McLennan et\u00a0al. \u201cData From LIDC\u2010IDRI \u201d 2015 https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX."},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106474"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNAS53565.2021.9628946"},{"key":"e_1_2_9_31_1","unstructured":"D.Reis J.Kupec J.Hong andA.Daoudi \u201cReal\u2010Time Flying Object Detection With YOLOv8 \u201d 2023 arXiv preprint arXiv:2305.09972."},{"key":"e_1_2_9_32_1","unstructured":"\u201cUltralytics \u201d6 2022 https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.2478\/ausi-2022-0004"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2021.3051910"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10897-x"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22890"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPAS50080.2020.9334937"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10453-z"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.3906\/elk-1304-36"},{"key":"e_1_2_9_40_1","first-page":"192","volume-title":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","author":"Goceri E.","year":"2012"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2015.229"},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-017-0666-z"},{"key":"e_1_2_9_43_1","volume-title":"A Comparative Evaluation for Liver Segmentation From Spir Images and a Novel Level Set Method Using Signed Pressure Force Function","author":"Goceri E.","year":"2013"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104949"},{"key":"e_1_2_9_45_1","first-page":"53","article-title":"Analysis of Capsule Networks for Image Classification","author":"Goceri E.","year":"2021","journal-title":"International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing"},{"key":"e_1_2_9_46_1","first-page":"29","article-title":"Capsule Neural Networks in Classification of Skin Lesions","author":"Goceri E.","year":"2021","journal-title":"International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.70023","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T10:26:14Z","timestamp":1738146374000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.70023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1002\/ima.70023"],"URL":"https:\/\/doi.org\/10.1002\/ima.70023","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"value":"0899-9457","type":"print"},{"value":"1098-1098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-07-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70023"}}